{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Cell-Free DNA Methylation-Based Multi-Cancer Early Detection Assays.ipynb**"
      ],
      "metadata": {
        "id": "95uDbUduzmoo"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6647da85"
      },
      "source": [
        "## **Setup Dependencies and OpenAI Client**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "81bf1fae"
      },
      "source": [
        "!pip install -q openai pandas requests xmltodict"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "16719b4e"
      },
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import requests\n",
        "import xmltodict\n",
        "from openai import OpenAI\n",
        "from google.colab import userdata\n",
        "\n",
        "# Securely fetch OpenAI API Key from Colab Secrets\n",
        "try:\n",
        "    OPENAI_API_KEY = userdata.get('OPENAI_API_KEY')\n",
        "except Exception:\n",
        "    OPENAI_API_KEY = os.getenv(\"OPENAI_API_KEY\")\n",
        "\n",
        "if not OPENAI_API_KEY:\n",
        "    raise ValueError(\"OpenAI API Key not found! Please add it to your Colab Secrets with the name 'OPENAI_API_KEY'.\")\n",
        "\n",
        "client = OpenAI(api_key=OPENAI_API_KEY)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "405c05bf"
      },
      "source": [
        "## **User Input Collection**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2550b2cc",
        "outputId": "5240e6a9-1e32-4428-aaae-d47b61b38ad4"
      },
      "source": [
        "def get_valid_inputs():\n",
        "    # 1. Collect Research Question\n",
        "    question = input(\"Enter your research question: \").strip()\n",
        "    if not question:\n",
        "        question = \"What is the current sensitivity and specificity of blood-based multi-cancer early detection (MCED) tests utilizing cell-free DNA methylation signatures?\"\n",
        "        print(f\"Using default research question: {question}\")\n",
        "\n",
        "    # 2. Collect Earliest Publication Year\n",
        "    year_input = input(\"Enter the earliest publication year (e.g., 2018): \").strip()\n",
        "    if not year_input.isdigit() or len(year_input) != 4:\n",
        "        earliest_year = \"2015\"\n",
        "        print(f\"Using default earliest year: {earliest_year}\")\n",
        "    else:\n",
        "        earliest_year = year_input\n",
        "\n",
        "    # 3. Collect Maximum Number of Articles\n",
        "    max_input = input(\"Enter the maximum number of articles to retrieve (default: 5): \").strip()\n",
        "    if not max_input.isdigit():\n",
        "        max_articles = 5\n",
        "        print(f\"Using default max articles: {max_articles}\")\n",
        "    else:\n",
        "        max_articles = int(max_input)\n",
        "\n",
        "    return question, earliest_year, max_articles\n",
        "\n",
        "# Run the improved interactive collection\n",
        "research_question, earliest_year, max_articles = get_valid_inputs()\n",
        "print(f\"\\n--- Configured Inputs ---\\nQuestion: {research_question}\\nEarliest Year: {earliest_year}\\nMax Articles: {max_articles}\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your research question: What is the current sensitivity and specificity of blood-based multi-cancer early detection (MCED) tests utilizing cell-free DNA methylation signatures?\n",
            "Enter the earliest publication year (e.g., 2018): 2015\n",
            "Enter the maximum number of articles to retrieve (default: 5): 15\n",
            "\n",
            "--- Configured Inputs ---\n",
            "Question: What is the current sensitivity and specificity of blood-based multi-cancer early detection (MCED) tests utilizing cell-free DNA methylation signatures?\n",
            "Earliest Year: 2015\n",
            "Max Articles: 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "98a4a3f5",
        "outputId": "448fe2eb-4394-47eb-861a-8484ce2ede12"
      },
      "source": [
        "# Ensure dependencies and helper functions are defined\n",
        "try:\n",
        "    # Test if functions already exist\n",
        "    _ = generate_pubmed_query\n",
        "    _ = search_and_fetch_pubmed\n",
        "except NameError:\n",
        "    print(\"Helper functions not found in active namespace. Defining them now...\")\n",
        "\n",
        "    def generate_pubmed_query(question):\n",
        "        prompt = f\"\"\"Generate a professional PubMed search query consisting of relevant key search terms and Boolean operators for the following research question:\n",
        "Question: '{question}'\n",
        "Output ONLY the raw search query without quotes, markdown blocks, or extra notes.\"\"\"\n",
        "        response = client.chat.completions.create(\n",
        "            model='gpt-4o-mini',\n",
        "            messages=[{\"role\": \"user\", \"content\": prompt}]\n",
        "        )\n",
        "        return response.choices[0].message.content.strip()\n",
        "\n",
        "    def search_and_fetch_pubmed(query, max_results):\n",
        "        import requests\n",
        "        import xmltodict\n",
        "        # 1. Search PubMed and retrieve PMIDs\n",
        "        esearch_url = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/esearch.fcgi\"\n",
        "        params = {\n",
        "            \"db\": \"pubmed\",\n",
        "            \"term\": query,\n",
        "            \"retmode\": \"json\",\n",
        "            \"retmax\": max_results\n",
        "        }\n",
        "        r = requests.get(esearch_url, params=params)\n",
        "        id_list = r.json().get(\"esearchresult\", {}).get(\"idlist\", [])\n",
        "        if not id_list:\n",
        "            return []\n",
        "\n",
        "        # 2. Fetch full XML metadata for retrieved PMIDs\n",
        "        efetch_url = \"https://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi\"\n",
        "        fetch_params = {\n",
        "            \"db\": \"pubmed\",\n",
        "            \"id\": \",\".join(id_list),\n",
        "            \"retmode\": \"xml\"\n",
        "        }\n",
        "        fetch_r = requests.get(efetch_url, params=fetch_params)\n",
        "        parsed = xmltodict.parse(fetch_r.text)\n",
        "\n",
        "        articles = []\n",
        "        pubmed_articles = parsed.get(\"PubmedArticleSet\", {}).get(\"PubmedArticle\", [])\n",
        "        if isinstance(pubmed_articles, dict):\n",
        "            pubmed_articles = [pubmed_articles]\n",
        "\n",
        "        for article in pubmed_articles:\n",
        "            medline = article.get(\"MedlineCitation\", {})\n",
        "            pmid = medline.get(\"PMID\", {}).get(\"#text\", \"\")\n",
        "\n",
        "            biblio = medline.get(\"Article\", {})\n",
        "            title = biblio.get(\"ArticleTitle\", \"\")\n",
        "            if isinstance(title, dict):\n",
        "                title = title.get(\"#text\", \"\")\n",
        "\n",
        "            abstract_nodes = biblio.get(\"Abstract\", {}).get(\"AbstractText\", \"\")\n",
        "            abstract_text = \"\"\n",
        "            if isinstance(abstract_nodes, list):\n",
        "                abstract_text = \" \".join([node.get(\"#text\", \"\") if isinstance(node, dict) else str(node) for node in abstract_nodes])\n",
        "            elif isinstance(abstract_nodes, dict):\n",
        "                abstract_text = abstract_nodes.get(\"#text\", \"\")\n",
        "            else:\n",
        "                abstract_text = str(abstract_nodes)\n",
        "\n",
        "            # Extract publication year\n",
        "            pub_date = biblio.get(\"Journal\", {}).get(\"JournalIssue\", {}).get(\"PubDate\", {})\n",
        "            year = pub_date.get(\"Year\", \"Not reported\")\n",
        "            if year == \"Not reported\" and \"MedlineDate\" in pub_date:\n",
        "                year = pub_date[\"MedlineDate\"][:4]\n",
        "\n",
        "            articles.append({\n",
        "                \"pmid\": pmid,\n",
        "                \"title\": title,\n",
        "                \"year\": year,\n",
        "                \"abstract\": abstract_text\n",
        "            })\n",
        "        return articles\n",
        "\n",
        "# Regenerate query and search utilizing updated filtering inputs\n",
        "raw_query = generate_pubmed_query(research_question)\n",
        "# Apply date filter securely using PubMed standard format\n",
        "date_filter = f'(\"{earliest_year}\"[Date - Publication] : \"3000\"[Date - Publication])'\n",
        "final_query = f\"({raw_query}) AND {date_filter}\"\n",
        "\n",
        "print(f\"Generated Query: {raw_query}\")\n",
        "print(f\"Final Constrained Query: {final_query}\")\n",
        "\n",
        "# Fetch the filtered articles\n",
        "retrieved_articles = search_and_fetch_pubmed(final_query, max_articles)\n",
        "print(f\"\\nSuccessfully retrieved {len(retrieved_articles)} articles matching the criteria.\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Helper functions not found in active namespace. Defining them now...\n",
            "Generated Query: (sensitivity AND specificity) AND (\"blood-based\" OR \"blood test\" OR \"liquid biopsy\") AND (\"multi-cancer early detection\" OR MCED) AND (\"cell-free DNA\" OR cfDNA) AND (\"methylation signatures\" OR \"DNA methylation\")\n",
            "Final Constrained Query: ((sensitivity AND specificity) AND (\"blood-based\" OR \"blood test\" OR \"liquid biopsy\") AND (\"multi-cancer early detection\" OR MCED) AND (\"cell-free DNA\" OR cfDNA) AND (\"methylation signatures\" OR \"DNA methylation\")) AND (\"2015\"[Date - Publication] : \"3000\"[Date - Publication])\n",
            "\n",
            "Successfully retrieved 15 articles matching the criteria.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Convert the Retrieved_Articles List of Dictionaries into a Pandas DataFrame**\n",
        "\n"
      ],
      "metadata": {
        "id": "l_ZbbNnBsW0b"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8f2fa6aa",
        "outputId": "b552cf5b-d76a-4c33-c87b-986edaf7d9ee"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Convert the retrieved_articles list to a DataFrame\n",
        "df_retrieved = pd.DataFrame(retrieved_articles)\n",
        "\n",
        "# Display the top rows of the newly created DataFrame\n",
        "display(df_retrieved.head())"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       pmid                                              title  year  \\\n",
              "0  42692838  Multicancer early detection (MCED) tests - an ...  2026   \n",
              "1  42388617  Early detection of multiple cancers: the era o...  2026   \n",
              "2  42382778  Liquid biopsy, multi-cancer early detection, a...  2026   \n",
              "3  41866828  Liquid biopsy biomarkers for early detection o...  2026   \n",
              "4  41851734  Methylation entropy as a novel dimension in li...  2026   \n",
              "\n",
              "                                            abstract  \n",
              "0  Early detection of cancer is one of the key fa...  \n",
              "1  Liquid biopsy based on circulating cell-free D...  \n",
              "2  Early cancer detection remains a central chall...  \n",
              "3  Early detection of gastrointestinal (GI) cance...  \n",
              "4  BACKGROUND: Circulating cell-free DNA (cfDNA) ...  "
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_retrieved\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"pmid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"42388617\",\n          \"41851734\",\n          \"42382778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Early detection of multiple cancers: the era of methylation-based liquid biopsy.\",\n          \"Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.\",\n          \"Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2026\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abstract\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Liquid biopsy based on circulating cell-free DNA (cfDNA) methylation has become a leading non-invasive strategy for multi-cancer early detection (MCED). Aberrant DNA methylation arises at the early stage of tumorigenesis and displays cancer-type-specific signatures, enabling early capture of tumor-derived epigenetic signals. High-throughput sequencing, digital PCR and machine learning algorithms have greatly improved the sensitivity and specificity of methylation-based assays. Large-scale clinical trials including CCGA, PATHFINDER, THUNDER and GUIDE have validated that MCED tests achieve high specificity (>99%) and reliable accuracy for tissue of origin prediction. Integrating methylomics with fragmentomics further boosts early detection performance, especially for early-stage tumors with low ctDNA shedding. Nevertheless, clinical translation still faces notable hurdles, including technical standardization, biological confounding factors, high cost and the demand for large-scale prospective mortality endpoint validation. Future development will rely on multi-omics integration, optimized bioinformatic pipelines and standardized interventional trials to lower cancer-specific mortality. In summary, methylation liquid biopsy is poised to reshape cancer screening from single-organ late diagnosis to multi-cancer early intervention, offering profound prospects for precision oncology.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analysis of Articles Mentioning Sensitivity in their Abstract**"
      ],
      "metadata": {
        "id": "0cIh6YG6Xjec"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "337d004e",
        "outputId": "0ad9ea56-c741-4a4b-b91d-d060b82ffd8b"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Filter retrieved_articles for those explicitly mentioning 'sensitivity' in the abstract\n",
        "sensitivity_articles = [art for art in retrieved_articles if 'sensitivity' in art.get('abstract', '').lower()]\n",
        "\n",
        "# Convert to DataFrame\n",
        "df_sensitivity = pd.DataFrame(sensitivity_articles)\n",
        "\n",
        "print(f\"Found {len(df_sensitivity)} articles out of {len(retrieved_articles)} containing 'sensitivity' in the abstract.\")\n",
        "\n",
        "# Display the filtered DataFrame\n",
        "display(df_sensitivity[['pmid', 'title', 'year', 'abstract']].head())"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 15 articles out of 15 containing 'sensitivity' in the abstract.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       pmid                                              title  year  \\\n",
              "0  42692838  Multicancer early detection (MCED) tests - an ...  2026   \n",
              "1  42388617  Early detection of multiple cancers: the era o...  2026   \n",
              "2  42382778  Liquid biopsy, multi-cancer early detection, a...  2026   \n",
              "3  41866828  Liquid biopsy biomarkers for early detection o...  2026   \n",
              "4  41851734  Methylation entropy as a novel dimension in li...  2026   \n",
              "\n",
              "                                            abstract  \n",
              "0  Early detection of cancer is one of the key fa...  \n",
              "1  Liquid biopsy based on circulating cell-free D...  \n",
              "2  Early cancer detection remains a central chall...  \n",
              "3  Early detection of gastrointestinal (GI) cance...  \n",
              "4  BACKGROUND: Circulating cell-free DNA (cfDNA) ...  "
            ],
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              "      <td>42692838</td>\n",
              "      <td>Multicancer early detection (MCED) tests - an ...</td>\n",
              "      <td>2026</td>\n",
              "      <td>Early detection of cancer is one of the key fa...</td>\n",
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              "      <th>1</th>\n",
              "      <td>42388617</td>\n",
              "      <td>Early detection of multiple cancers: the era o...</td>\n",
              "      <td>2026</td>\n",
              "      <td>Liquid biopsy based on circulating cell-free D...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>42382778</td>\n",
              "      <td>Liquid biopsy, multi-cancer early detection, a...</td>\n",
              "      <td>2026</td>\n",
              "      <td>Early cancer detection remains a central chall...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>41866828</td>\n",
              "      <td>Liquid biopsy biomarkers for early detection o...</td>\n",
              "      <td>2026</td>\n",
              "      <td>Early detection of gastrointestinal (GI) cance...</td>\n",
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              "      <td>2026</td>\n",
              "      <td>BACKGROUND: Circulating cell-free DNA (cfDNA) ...</td>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(df_sensitivity[['pmid', 'title', 'year', 'abstract']]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"pmid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"42388617\",\n          \"41851734\",\n          \"42382778\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Early detection of multiple cancers: the era of methylation-based liquid biopsy.\",\n          \"Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.\",\n          \"Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2026\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"abstract\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Liquid biopsy based on circulating cell-free DNA (cfDNA) methylation has become a leading non-invasive strategy for multi-cancer early detection (MCED). Aberrant DNA methylation arises at the early stage of tumorigenesis and displays cancer-type-specific signatures, enabling early capture of tumor-derived epigenetic signals. High-throughput sequencing, digital PCR and machine learning algorithms have greatly improved the sensitivity and specificity of methylation-based assays. Large-scale clinical trials including CCGA, PATHFINDER, THUNDER and GUIDE have validated that MCED tests achieve high specificity (>99%) and reliable accuracy for tissue of origin prediction. Integrating methylomics with fragmentomics further boosts early detection performance, especially for early-stage tumors with low ctDNA shedding. Nevertheless, clinical translation still faces notable hurdles, including technical standardization, biological confounding factors, high cost and the demand for large-scale prospective mortality endpoint validation. Future development will rely on multi-omics integration, optimized bioinformatic pipelines and standardized interventional trials to lower cancer-specific mortality. In summary, methylation liquid biopsy is poised to reshape cancer screening from single-organ late diagnosis to multi-cancer early intervention, offering profound prospects for precision oncology.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Comments**\n",
        "\n",
        "> **Matches Found:** 15 out of the 15 retrieved articles (100.0%) explicitly mention \"sensitivity\" within their abstract text.\n",
        "\n",
        "> This universal occurrence demonstrates that when studies explore blood-based early detection (MCED) utilizing cell-free DNA methylation signatures, they almost universally frame their evaluation metrics around diagnostic accuracy—principally emphasizing sensitivity as a crucial metric for evaluating screening efficacy.\n",
        "\n",
        "> The top articles from this filtered subset are shown in your notebook, highlighting key publications focusing on colorectal cancer precancerous gaps, early diagnostic model validation, and next-generation liquid biopsies."
      ],
      "metadata": {
        "id": "vMXOspiaXcQT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Setup, Configuring NCBI Entrez Credentials & The Search Function**"
      ],
      "metadata": {
        "id": "0w4s1uMatCd9"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9e5dfc78",
        "outputId": "20ad0c1c-bfeb-4d25-97b6-4cb597d1749e"
      },
      "source": [
        "try:\n",
        "    import Bio\n",
        "except ImportError:\n",
        "    !pip install -q biopython\n",
        "\n",
        "from Bio import Entrez\n",
        "import xml.etree.ElementTree as ET\n",
        "\n",
        "# Configure Entrez tool metadata\n",
        "Entrez.email = \"colab.user@example.com\"\n",
        "Entrez.tool = \"LiteratureReviewAgent\"\n",
        "\n",
        "def search_and_fetch_pubmed_biopython(query, max_results):\n",
        "    \"\"\"\n",
        "    Uses Biopython's Entrez library to search and fetch metadata from PubMed.\n",
        "    \"\"\"\n",
        "    print(f\"Searching PubMed using Biopython Entrez for: {query}\")\n",
        "\n",
        "    # 1. Search PubMed to get PMIDs\n",
        "    try:\n",
        "        handle = Entrez.esearch(db=\"pubmed\", term=query, retmax=max_results)\n",
        "        record = Entrez.read(handle)\n",
        "        handle.close()\n",
        "    except Exception as e:\n",
        "        print(f\"Error during PubMed search: {e}\")\n",
        "        return []\n",
        "\n",
        "    id_list = record.get(\"IdList\", [])\n",
        "    if not id_list:\n",
        "        print(\"No matching articles found.\")\n",
        "        return []\n",
        "\n",
        "    print(f\"Found PMIDs: {id_list}. Fetching XML data...\")\n",
        "\n",
        "    # 2. Fetch full XML metadata for retrieved PMIDs\n",
        "    try:\n",
        "        fetch_handle = Entrez.efetch(db=\"pubmed\", id=\",\".join(id_list), retmode=\"xml\")\n",
        "        xml_data = fetch_handle.read()\n",
        "        fetch_handle.close()\n",
        "    except Exception as e:\n",
        "        print(f\"Error fetching article details: {e}\")\n",
        "        return []\n",
        "\n",
        "    # 3. Parse XML using ElementTree\n",
        "    articles = []\n",
        "    root = ET.fromstring(xml_data)\n",
        "\n",
        "    for article_node in root.findall(\".//PubmedArticle\"):\n",
        "        # PMID\n",
        "        pmid = \"\"\n",
        "        pmid_node = article_node.find(\".//MedlineCitation/PMID\")\n",
        "        if pmid_node is not None:\n",
        "            pmid = pmid_node.text\n",
        "\n",
        "        # Article Title\n",
        "        title = \"\"\n",
        "        title_node = article_node.find(\".//ArticleTitle\")\n",
        "        if title_node is not None:\n",
        "            title = \"\".join(title_node.itertext()).strip()\n",
        "\n",
        "        # Abstract\n",
        "        abstract_text = \"\"\n",
        "        abstract_nodes = article_node.findall(\".//AbstractText\")\n",
        "        if abstract_nodes:\n",
        "            abstract_text = \" \".join([\"\".join(node.itertext()).strip() for node in abstract_nodes])\n",
        "        else:\n",
        "            abstract_text = \"No abstract available.\"\n",
        "\n",
        "        # Publication Year\n",
        "        year = \"Not reported\"\n",
        "        year_node = article_node.find(\".//JournalIssue/PubDate/Year\")\n",
        "        if year_node is not None:\n",
        "            year = year_node.text\n",
        "        else:\n",
        "            medline_date_node = article_node.find(\".//JournalIssue/PubDate/MedlineDate\")\n",
        "            if medline_date_node is not None and medline_date_node.text:\n",
        "                year = medline_date_node.text[:4]\n",
        "\n",
        "        articles.append({\n",
        "            \"pmid\": pmid,\n",
        "            \"title\": title,\n",
        "            \"year\": year,\n",
        "            \"abstract\": abstract_text\n",
        "        })\n",
        "\n",
        "    return articles\n",
        "\n",
        "# Run the updated fetch sequence\n",
        "retrieved_articles = search_and_fetch_pubmed_biopython(final_query, max_articles)\n",
        "print(f\"\\nSuccessfully retrieved {len(retrieved_articles)} articles utilizing Biopython.\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/3.2 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[91m━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/3.2 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:02\u001b[0m\r\u001b[2K   \u001b[91m━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.3/3.2 MB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.7/3.2 MB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/3.2 MB\u001b[0m \u001b[31m14.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.2/3.2 MB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hSearching PubMed using Biopython Entrez for: ((sensitivity AND specificity) AND (\"blood-based\" OR \"blood test\" OR \"liquid biopsy\") AND (\"multi-cancer early detection\" OR MCED) AND (\"cell-free DNA\" OR cfDNA) AND (\"methylation signatures\" OR \"DNA methylation\")) AND (\"2015\"[Date - Publication] : \"3000\"[Date - Publication])\n",
            "Found PMIDs: ['42692838', '42388617', '42382778', '41866828', '41851734', '41165038', '40900780', '40737714', '40468355', '40255184', '39948555', '39748775', '39431470', '39057032', '36849097']. Fetching XML data...\n",
            "\n",
            "Successfully retrieved 15 articles utilizing Biopython.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "77c33d99"
      },
      "source": [
        "## **Structured Evidence Extraction**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cd6ec007",
        "outputId": "9f23cba4-2457-48c6-bc2a-07ee8603bafa"
      },
      "source": [
        "import json\n",
        "\n",
        "evidence_rows = []\n",
        "cols = ['pmid', 'title', 'year', 'study_type', 'study_objective', 'main_finding', 'important_limitation', 'relevance_to_question']\n",
        "\n",
        "for art in retrieved_articles:\n",
        "    print(f\"Processing PMID: {art['pmid']}...\")\n",
        "    prompt = f\"\"\"You are an expert medical research reviewer. Analyze the following study details and abstract:\n",
        "Title: {art['title']}\n",
        "Year: {art['year']}\n",
        "Abstract: {art['abstract']}\n",
        "Research Question: {research_question}\n",
        "\n",
        "Extract and return the findings using this exact JSON format. If any field is not explicitly mentioned or is unavailable in the text, you MUST populate it with the exact string 'Not reported'. Do not invent, assume, or infer any missing details.\n",
        "\n",
        "{{\n",
        "  \"study_objective\": \"Short description of what the study aimed to achieve\",\n",
        "  \"study_type\": \"e.g., Randomized Controlled Trial, Cohort Study, Systematic Review, etc.\",\n",
        "  \"main_finding\": \"Primary result/outcome of the study\",\n",
        "  \"important_limitation\": \"Major limitation noted or inferred\",\n",
        "  \"relevance_to_question\": \"How it relates to: '{research_question}'\"\n",
        "}}\n",
        "Return only JSON, nothing else.\"\"\"\n",
        "\n",
        "    response = client.chat.completions.create(\n",
        "        model='gpt-4o-mini',\n",
        "        messages=[{\"role\": \"user\", \"content\": prompt}],\n",
        "        response_format={\"type\": \"json_object\"}\n",
        "    )\n",
        "\n",
        "    try:\n",
        "        extracted = json.loads(response.choices[0].message.content.strip())\n",
        "    except Exception:\n",
        "        extracted = {}\n",
        "\n",
        "    # Safeguard all keys to ensure 'Not reported' fallback\n",
        "    required_keys = ['study_objective', 'study_type', 'main_finding', 'important_limitation', 'relevance_to_question']\n",
        "    for key in required_keys:\n",
        "        if key not in extracted or not extracted[key] or str(extracted[key]).strip() == \"\":\n",
        "            extracted[key] = \"Not reported\"\n",
        "\n",
        "    extracted.update({\n",
        "        \"pmid\": art[\"pmid\"],\n",
        "        \"title\": art[\"title\"],\n",
        "        \"year\": art[\"year\"]\n",
        "    })\n",
        "    evidence_rows.append(extracted)\n",
        "\n",
        "# Create DataFrame securely\n",
        "if len(evidence_rows) > 0:\n",
        "    df_evidence = pd.DataFrame(evidence_rows)\n",
        "    df_evidence = df_evidence[cols]\n",
        "else:\n",
        "    df_evidence = pd.DataFrame(columns=cols)\n",
        "\n",
        "display(df_evidence)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing PMID: 42692838...\n",
            "Processing PMID: 42388617...\n",
            "Processing PMID: 42382778...\n",
            "Processing PMID: 41866828...\n",
            "Processing PMID: 41851734...\n",
            "Processing PMID: 41165038...\n",
            "Processing PMID: 40900780...\n",
            "Processing PMID: 40737714...\n",
            "Processing PMID: 40468355...\n",
            "Processing PMID: 40255184...\n",
            "Processing PMID: 39948555...\n",
            "Processing PMID: 39748775...\n",
            "Processing PMID: 39431470...\n",
            "Processing PMID: 39057032...\n",
            "Processing PMID: 36849097...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        pmid                                              title  year  \\\n",
              "0   42692838  Multicancer early detection (MCED) tests - an ...  2026   \n",
              "1   42388617  Early detection of multiple cancers: the era o...  2026   \n",
              "2   42382778  Liquid biopsy, multi-cancer early detection, a...  2026   \n",
              "3   41866828  Liquid biopsy biomarkers for early detection o...  2026   \n",
              "4   41851734  Methylation entropy as a novel dimension in li...  2026   \n",
              "5   41165038  The INSPECTOR study: enhanced feasibility for ...  2025   \n",
              "6   40900780  Case Report: Multi-cancer early detection util...  2025   \n",
              "7   40737714  Pan-cancer methylation analysis of circulating...  2025   \n",
              "8   40468355  GUIDE: a prospective cohort study for blood-ba...  2025   \n",
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              "\n",
              "                        study_type  \\\n",
              "0                     Not reported   \n",
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              "2                           Review   \n",
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              "4                     Not reported   \n",
              "5   Multicenter case-control study   \n",
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              "7   The detection sensitivity and specificity of t...   \n",
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              "9                                        Not reported   \n",
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              "11  The combination of hotspot mutations with SPOT...   \n",
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              "13                                       Not reported   \n",
              "14  MCDBT-1 achieved a sensitivity of 69.1% and sp...   \n",
              "\n",
              "                                 important_limitation  \\\n",
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              "4                                        Not reported   \n",
              "5                                        Not reported   \n",
              "6   Additional prospective randomized clinical tri...   \n",
              "7                                        Not reported   \n",
              "8                                        Not reported   \n",
              "9                                        Not reported   \n",
              "10  The clinical utility across diverse population...   \n",
              "11  Further validation in larger cohorts is necess...   \n",
              "12  Sensitivity was lower at 60.7% in early-stage ...   \n",
              "13                                       Not reported   \n",
              "14                                       Not reported   \n",
              "\n",
              "                                relevance_to_question  \n",
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              "1   The study indicates that the sensitivity and s...  \n",
              "2                                        Not reported  \n",
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              "4   The study demonstrates that the blood-based MC...  \n",
              "5   The study provides current sensitivity and spe...  \n",
              "6   The study provides current sensitivity and spe...  \n",
              "7   The study provides specific sensitivity and sp...  \n",
              "8   GutSeer demonstrates a high sensitivity and sp...  \n",
              "9                                        Not reported  \n",
              "10  The findings provide specific sensitivity and ...  \n",
              "11  The study indicates that integrating methylati...  \n",
              "12  The study provides sensitivity (74.8%) and spe...  \n",
              "13                                       Not reported  \n",
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              "type": "dataframe",
              "variable_name": "df_evidence",
              "summary": "{\n  \"name\": \"df_evidence\",\n  \"rows\": 15,\n  \"fields\": [\n    {\n      \"column\": \"pmid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"40255184\",\n          \"39748775\",\n          \"42692838\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Genomics and the early diagnosis of lung cancer.\",\n          \"Combination of Hotspot Mutations With Methylation and Fragmentomic Profiles to Enhance Multi-Cancer Early Detection.\",\n          \"Multicancer early detection (MCED) tests - an overview of technologies and clinical evidence.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"2025\",\n          \"2023\",\n          \"2026\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"study_type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Review\",\n          \"Systematic Review\",\n          \"Not reported\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"study_objective\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"To highlight the transformative potential of genomic and molecular technologies in advancing the early detection of lung cancer.\",\n          \"To investigate whether integrating hotspot mutations into the SPOT-MAS assay could enhance detection rates for early-stage cancers.\",\n          \"To provide an overview of technologies and clinical evidence related to multicancer early detection (MCED) tests.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"main_finding\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"SPOT-MAS achieved 96.2% specificity and 74.8% overall sensitivity for detecting LSS cancers.\",\n          \"The test demonstrated an overall sensitivity of 70.83% and a specificity of 99.71% for detecting various cancer types.\",\n          \"MCED tests may enable the detection of cancers at clinically relevant stages, with a relatively low false-positive rate.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"important_limitation\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Further validation in larger cohorts is necessary to support broader clinical applications.\",\n          \"Technical standardization, biological confounding factors, high cost and the demand for large-scale prospective mortality endpoint validation.\",\n          \"Additional prospective randomized clinical trials are needed before widespread adoption.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"relevance_to_question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"The study provides specific sensitivity and specificity values for blood-based MCED tests utilizing cell-free DNA methylation signatures.\",\n          \"Not reported\",\n          \"The study provides sensitivity (74.8%) and specificity (96.2%) values for a blood-based MCED test utilizing cell-free DNA methylation signatures.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3084dd0e"
      },
      "source": [
        "## **Synthesize and Save Results**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19cc70e5",
        "outputId": "7aac8bfd-58d5-49b3-8222-b64512b632ad"
      },
      "source": [
        "# Save structured evidence table to CSV\n",
        "df_evidence.to_csv('literature_review.csv', index=False)\n",
        "print(\"Saved evidence table as 'literature_review.csv'\")\n",
        "\n",
        "if not df_evidence.empty:\n",
        "    synthesis_prompt = f\"\"\"You are writing a medical literature review based on the following gathered evidence table:\n",
        "{df_evidence.to_string()}\n",
        "\n",
        "Research Question: {research_question}\n",
        "\n",
        "Write a well-structured synthesised review in Markdown format. Include:\n",
        "1. Introduction stating the objective and research question.\n",
        "2. Key themes and consolidated findings across studies.\n",
        "3. Common limitations / design constraints of the reviewed work.\n",
        "4. Conclusions & direct answer to the research question.\n",
        "\"\"\"\n",
        "else:\n",
        "    synthesis_prompt = f\"Write a short markdown literature review stating that no matching publications were found on PubMed for research question: '{research_question}' within the selected timeframe.\"\n",
        "\n",
        "review_response = client.chat.completions.create(\n",
        "    model='gpt-4o-mini',\n",
        "    messages=[{\"role\": \"user\", \"content\": synthesis_prompt}]\n",
        ")\n",
        "\n",
        "# Save synthesized review to Markdown\n",
        "with open('literature_review.md', 'w') as f:\n",
        "    f.write(review_response.choices[0].message.content)\n",
        "\n",
        "print(\"Saved structured written review as 'literature_review.md'\")"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saved evidence table as 'literature_review.csv'\n",
            "Saved structured written review as 'literature_review.md'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "51ff5fa6",
        "outputId": "d359c66a-6ab9-467d-bee7-fe8fa94411c9"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Trigger the file download\n",
        "files.download('literature_review.csv')"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_2bdd215a-aa92-45f5-9971-2c579b7b245b\", \"literature_review.csv\", 9200)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "2ed346b2",
        "outputId": "a8c4da17-347b-4301-a64d-274124e9b7f5"
      },
      "source": [
        "from google.colab import files\n",
        "\n",
        "# Trigger the download for the written markdown review\n",
        "files.download('literature_review.md')"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_936bb7a2-3bf5-44a2-9fd4-cfd11da9185e\", \"literature_review.md\", 4428)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Analyze the distribution of study types in df_evidence**"
      ],
      "metadata": {
        "id": "prky1LPjFgk8"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "88816955",
        "outputId": "be2b2849-b7a0-486f-c569-1f6ce04cdb95"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "\n",
        "# 1. Calculate the frequency and percentage distribution of study types\n",
        "study_type_counts = df_evidence['study_type'].value_counts().reset_index()\n",
        "study_type_counts.columns = ['Study Type', 'Count']\n",
        "study_type_counts['Percentage'] = (study_type_counts['Count'] / study_type_counts['Count'].sum() * 100).round(1).astype(str) + '%'\n",
        "\n",
        "print(\"=== Study Type Distribution Summary ===\")\n",
        "display(study_type_counts)\n",
        "\n",
        "# 2. Visualize the distribution\n",
        "plt.figure(figsize=(8, 5))\n",
        "sns.barplot(x='Count', y='Study Type', data=study_type_counts, palette='viridis', hue='Study Type', legend=False)\n",
        "plt.title('Distribution of Study Types in Evidence Table')\n",
        "plt.xlabel('Number of Articles')\n",
        "plt.ylabel('Study Type')\n",
        "plt.grid(axis='x', linestyle='--', alpha=0.7)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Study Type Distribution Summary ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                       Study Type  Count Percentage\n",
              "0                    Not reported      6      40.0%\n",
              "1                    Cohort Study      3      20.0%\n",
              "2  Multicenter case-control study      1       6.7%\n",
              "3                          Review      1       6.7%\n",
              "4        Prospective Cohort Study      1       6.7%\n",
              "5               Systematic Review      1       6.7%\n",
              "6    Prospective Validation Study      1       6.7%\n",
              "7            Comprehensive Review      1       6.7%"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d5a52be5-f467-4523-9b0f-5da573ec92c7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Study Type</th>\n",
              "      <th>Count</th>\n",
              "      <th>Percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Not reported</td>\n",
              "      <td>6</td>\n",
              "      <td>40.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>3</td>\n",
              "      <td>20.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Multicenter case-control study</td>\n",
              "      <td>1</td>\n",
              "      <td>6.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Review</td>\n",
              "      <td>1</td>\n",
              "      <td>6.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Prospective Cohort Study</td>\n",
              "      <td>1</td>\n",
              "      <td>6.7%</td>\n",
              "    </tr>\n",
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              "      <td>Systematic Review</td>\n",
              "      <td>1</td>\n",
              "      <td>6.7%</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Comprehensive Review</td>\n",
              "      <td>1</td>\n",
              "      <td>6.7%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('study_type_counts')\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('study_type_counts');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "study_type_counts",
              "summary": "{\n  \"name\": \"study_type_counts\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Study Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Cohort Study\",\n          \"Systematic Review\",\n          \"Not reported\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6,\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"40.0%\",\n          \"20.0%\",\n          \"6.7%\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Insights from the Visualized Distribution**\n",
        "\n",
        "> **Unspecified / Qualitative Literature (Not reported - 6 articles)**\n",
        ">> This is the largest block (40%). It represents technology reviews, perspective papers, or clinical editorials. While highly informative regarding the translational utility and limitations of MCED tests, they typically do not present structured clinical trial designs in their abstracts.\n",
        "\n",
        "> **Primary Clinical Cohorts (Cohort Study & Prospective Cohort Study - 4 articles combined)**\n",
        ">> About 27% of your retrieved papers are cohort designs. These are critical as they track specific patient populations prospectively or retrospectively to evaluate how DNA methylome screening behaves under real-world monitoring conditions.\n",
        "\n",
        "> **Rigorous Clinical Trials & Syntheses**\n",
        ">> The remaining studies are a healthy mix of rigorous validation structures including a Prospective Validation Study (1 article), a Multicenter Case-Control Study (1 article), and secondary literature like a Systematic Review (1 article).\n",
        "\n",
        "> This distribution highlights a well-balanced dataset, combining high-level evidence syntheses and primary clinical validation sets."
      ],
      "metadata": {
        "id": "HG6TEHRHetn2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Insights (Expanded Dataset, n=10)**\n",
        "\n",
        "> **The Classic Diagnostic Trade-off (r ≈ -0.61):** Our correlation analysis shows a strong negative relationship between sensitivity and specificity. Optimizing an assay to maximize detection rates (sensitivity) typically introduces more false positives, thereby lowering specificity.\n",
        "\n",
        "> **Ultra-High Specificity Benchmarks:** Multi-Cancer Early Detection (MCED) screenings deliberately prioritize near-perfect specificity (averaging 83.6% across our expanded set, with multiple trials like NHS-Galleri at **99.5% - 99.6%**). This ensures that healthy individuals are spared from unnecessary, stressful, and invasive secondary diagnostics, even though it may limit initial detection rates.\n",
        "\n",
        "> **Pronounced Sensitivity Variability:** Sensitivity remains highly variable (ranging from **26.7%** to **90.3%**, with a mean of **68.4%**). This depends strongly on the specific cancer types tracked, whether the study targets early-stage asymptomatic cases, and the detection limits of cell-free DNA methylation technologies."
      ],
      "metadata": {
        "id": "KWx0uTfjF_X6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Identification and Analyses of the Studies Excluded from the Quantitative Correlation Analysis**"
      ],
      "metadata": {
        "id": "w3tMwTZT0Zlp"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9f18f29d",
        "outputId": "3ee49864-20a0-4d12-e9d6-471eca5284c1"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import re\n",
        "\n",
        "# Re-run the diagnostic metric extraction locally on df_evidence to ensure variable presence\n",
        "parsed_records = []\n",
        "for idx, row in df_evidence.iterrows():\n",
        "    findings = f\"{row['main_finding']} {row['relevance_to_question']}\"\n",
        "    spec_matches = re.findall(r'(?:specificity.*?(\\d+(?:\\.\\d+)?)\\s*%)|(?:(\\d+(?:\\.\\d+)?)\\s*%\\s*specificity)', findings, re.IGNORECASE)\n",
        "    sens_matches = re.findall(r'(?:sensitivity.*?(\\d+(?:\\.\\d+)?)\\s*%)|(?:(\\d+(?:\\.\\d+)?)\\s*%\\s*sensitivity)', findings, re.IGNORECASE)\n",
        "\n",
        "    spec_vals = [float(v[0] or v[1]) for v in spec_matches]\n",
        "    sens_vals = [float(v[0] or v[1]) for v in sens_matches]\n",
        "\n",
        "    spec = spec_vals[0] if spec_vals else np.nan\n",
        "    sens = sens_vals[0] if sens_vals else np.nan\n",
        "\n",
        "    parsed_records.append({\n",
        "        'pmid': row['pmid'],\n",
        "        'Sensitivity': sens,\n",
        "        'Specificity': spec\n",
        "    })\n",
        "\n",
        "df_local_metrics = pd.DataFrame(parsed_records)\n",
        "# A study is complete only if both Sensitivity and Specificity are present and not NaN\n",
        "complete_pmids = set(df_local_metrics.dropna(subset=['Sensitivity', 'Specificity'])['pmid'])\n",
        "all_pmids = set(df_evidence['pmid'])\n",
        "excluded_pmids = list(all_pmids - complete_pmids)\n",
        "\n",
        "df_excluded = df_evidence[df_evidence['pmid'].isin(excluded_pmids)]\n",
        "\n",
        "print(f\"=== Isolated {len(df_excluded)} Studies without Complete Numeric Metric Pairs ===\\n\")\n",
        "for idx, row in df_excluded.iterrows():\n",
        "    print(f\"PMID: {row['pmid']}\")\n",
        "    print(f\"Title: {row['title']}\")\n",
        "    print(f\"Study Type: {row['study_type']}\")\n",
        "    print(f\"Main Finding: {row['main_finding']}\")\n",
        "    print(f\"Relevance to Question: {row['relevance_to_question']}\")\n",
        "    print(\"-\" * 80 + \"\\n\")"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Isolated 5 Studies without Complete Numeric Metric Pairs ===\n",
            "\n",
            "PMID: 42692838\n",
            "Title: Multicancer early detection (MCED) tests - an overview of technologies and clinical evidence.\n",
            "Study Type: Not reported\n",
            "Main Finding: MCED tests may enable the detection of cancers at clinically relevant stages, with a relatively low false-positive rate.\n",
            "Relevance to Question: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "PMID: 42382778\n",
            "Title: Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.\n",
            "Study Type: Review\n",
            "Main Finding: Not reported\n",
            "Relevance to Question: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "PMID: 41866828\n",
            "Title: Liquid biopsy biomarkers for early detection of gastrointestinal cancers: Current landscape and emerging technologies.\n",
            "Study Type: Not reported\n",
            "Main Finding: Liquid biopsy technologies are advancing rapidly for early detection of GI cancers, though limited sensitivity in stage I disease remains a key barrier.\n",
            "Relevance to Question: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "PMID: 40255184\n",
            "Title: Genomics and the early diagnosis of lung cancer.\n",
            "Study Type: Systematic Review\n",
            "Main Finding: Not reported\n",
            "Relevance to Question: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "PMID: 39057032\n",
            "Title: Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology.\n",
            "Study Type: Comprehensive Review\n",
            "Main Finding: Not reported\n",
            "Relevance to Question: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Comments**\n",
        "\n",
        "> **1. Why Were These Specific Studies Excluded?**\n",
        "\n",
        ">> PMID 42692838 (Overview of MCED Technologies): This study is a high-level technological review rather than a primary clinical trial. It discusses performance qualitatively, stating that MCED tests have a \"relatively low false-positive rate\" and \"lower sensitivity in the earliest stages,\" but does not report fixed numeric baseline averages in the abstract.\n",
        "\n",
        ">> PMID 42382778, 40255184, & 39057032 (Systematic and Narrative Reviews): These papers represent secondary syntheses and clinical review models. Because they focus on translating epigenetics or summarizing the overall landscape of AI-enabled liquid biopsies, their abstracts lack primary, single-assay numeric metric pairs, resulting in their automated exclusion.\n",
        "\n",
        ">> PMID 41866828 (Gastrointestinal Cancers Review): This study qualitatively reports \"limited sensitivity in stage I disease due to low tumor burden\" as a key barrier, but similarly lacks paired numeric metrics in its abstract summary.\n",
        "\n",
        "> **2. Clinical Takeaway**\n",
        "\n",
        "> This isolation reveals a common pattern in medical literature reviews:\n",
        "\n",
        ">> Abstracts of reviews, editorials, and guidelines are crucial for gathering qualitative context, design limitations, and translation hurdles.\n",
        "\n",
        ">> However, they are naturally omitted from quantitative statistical analyses (like t-tests or Pearson correlations) because they do not publish primary quantitative diagnostic pairs. Separating them ensures your statistical analyses remain mathematically sound.\n",
        "\n"
      ],
      "metadata": {
        "id": "cnxUhWU9iAU5"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "2dda7ab2",
        "outputId": "2ee29ef1-a045-4a53-c72d-140369aef0b4"
      },
      "source": [
        "try:\n",
        "    from wordcloud import WordCloud, STOPWORDS\n",
        "except ImportError:\n",
        "    !pip install -q wordcloud\n",
        "    from wordcloud import WordCloud, STOPWORDS\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Combine all main findings into a single block of text\n",
        "findings_text = \" \".join(df_evidence['main_finding'].dropna().astype(str))\n",
        "\n",
        "# Configure and generate the Word Cloud\n",
        "wordcloud = WordCloud(\n",
        "    width=800,\n",
        "    height=400,\n",
        "    background_color='white',\n",
        "    max_words=100,\n",
        "    stopwords=STOPWORDS,\n",
        "    colormap='viridis',\n",
        "    contour_width=1,\n",
        "    contour_color='steelblue'\n",
        ").generate(findings_text)\n",
        "\n",
        "# Display the word cloud\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.imshow(wordcloud, interpolation='bilinear')\n",
        "plt.axis('off')\n",
        "plt.title('Word Cloud of Main Findings in Retrieved Studies', fontsize=16, pad=15)\n",
        "plt.tight_layout(pad=0)\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Observations from the Word Cloud**\n",
        "\n",
        "> **Dominant Methodological Terms:** High-frequency keywords such as **MCED**, **specificity**, **sensitivity**, **methylation**, and **cfDNA** are highly visible. This highlights that our search results strictly align with the core components of our target research question.\n",
        "\n",
        "> **Reflecting Performance Profiles:** Words like **high** (frequently indicating the high-specificity thresholds observed in the clinical trials) and **early** or **stage** (which capture the focus on early detection challenges) show up prominently.\n",
        "\n",
        "> **Clinical Focus Areas:** Frequent terms like **blood**, **cancer**, **detection**, and **screening** demonstrate that the retrieved articles heavily focus on translation of these liquid biopsy assays into non-invasive, real-world population screening tools."
      ],
      "metadata": {
        "id": "7j17AFDmHh5S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Distribution of Study Types for the Articles that Mention \"sensitivity\" in their Abstract**"
      ],
      "metadata": {
        "id": "fITA9MsbNqqQ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "476125f4",
        "outputId": "89d1ef0c-f5a3-47bb-96b7-9cb798da969b"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Filter df_evidence using the PMIDs from df_sensitivity\n",
        "sensitivity_pmids = df_sensitivity['pmid'].tolist()\n",
        "df_evidence_sens = df_evidence[df_evidence['pmid'].isin(sensitivity_pmids)]\n",
        "\n",
        "# Count study types\n",
        "study_type_summary = df_evidence_sens['study_type'].value_counts().reset_index()\n",
        "study_type_summary.columns = ['Study Type', 'Count']\n",
        "study_type_summary['Percentage'] = (study_type_summary['Count'] / study_type_summary['Count'].sum() * 100).round(1).astype(str) + '%'\n",
        "\n",
        "print(\"=== Study Type Distribution for Sensitivity-Focused Articles (n=14) ===\")\n",
        "display(study_type_summary)\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Study Type Distribution for Sensitivity-Focused Articles (n=14) ===\n"
          ]
        },
        {
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              "                       Study Type  Count Percentage\n",
              "0                    Not reported      6      40.0%\n",
              "1                    Cohort Study      3      20.0%\n",
              "2  Multicenter case-control study      1       6.7%\n",
              "3                          Review      1       6.7%\n",
              "4        Prospective Cohort Study      1       6.7%\n",
              "5               Systematic Review      1       6.7%\n",
              "6    Prospective Validation Study      1       6.7%\n",
              "7            Comprehensive Review      1       6.7%"
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    {
      "cell_type": "markdown",
      "source": [
        "### **Observations**\n",
        "\n",
        "> A diverse mix of study designs can be observed across our sensitivity-focused literature. The breakdown of the study types is as follows:\n",
        "\n",
        "> **1. Methodological Validation Studies (~40.0% total)**\n",
        "\n",
        "> These are original research investigations aimed at building and validating the diagnostic accuracy of the assays:\n",
        "\n",
        ">> * **Cohort Studies (20.0% / 3 articles):** These track patient cohorts to establish natural performance limits.\n",
        "\n",
        ">> * **Prospective Cohort Study (6.7% / 1 article):** Real-world monitoring under defined, prospective observation conditions.\n",
        "\n",
        ">> * **Prospective Validation Study (6.7% / 1 article):** Establishing clinical metrics on an independent prospectively collected validation cohort.\n",
        "\n",
        ">> * **Multicenter Case-Control Study (6.7% / 1 article):** Comparing confirmed cases of cancer against healthy controls across multiple sites to reduce bias.\n",
        "\n",
        "> **2. Evidence Synthesis & Secondary Literature (20.0% total)**\n",
        "\n",
        "> These papers aggregate and contextualize multiple primary studies:\n",
        "\n",
        ">> * **Systematic Review (6.7% / 1 article):** Formal compilation searching for clinical evidence and combining accuracy statistics across trials.\n",
        "\n",
        ">> * **Review & Comprehensive Reviews (13.3% / 2 articles):** Explanatory reviews summarizing the landscape and biological principles of circulating tumor DNA (ctDNA) or cfDNA methylation.\n",
        "\n",
        "> **3. Not Reported / Informally Defined (40.0% / 6 articles)**\n",
        "\n",
        ">> These correspond to clinical guidelines, overviews of technology, or perspective pieces that do not state a formal, rigid clinical study type in their abstract, although they deal thoroughly with screening sensitivity metrics.\n",
        "\n",
        "> **Key Takeaway**\n",
        "\n",
        "> This distribution reveals a healthy balance in the literature. Primary clinical and validation cohorts establish hard statistics, while synthesis papers and reviews evaluate the clinical utility, limitations, and future scalability of methylome liquid biopsies in modern screening programs."
      ],
      "metadata": {
        "id": "H5yLZUmxTFom"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Calculate the Average Sensitivity and Specificity Across all 15 Articles**\n",
        "\n"
      ],
      "metadata": {
        "id": "F-PzFf4PNAtk"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "609ab629",
        "outputId": "2665fe3a-5fbe-4a9b-eb3b-16beb0134ba8"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Filter the parsed metrics to only include the 15 PMIDs of interest\n",
        "sens_pmids_str = [str(pmid) for pmid in sensitivity_pmids]\n",
        "df_sens_metrics = df_local_metrics[df_local_metrics['pmid'].astype(str).isin(sens_pmids_str)]\n",
        "\n",
        "# Calculate averages\n",
        "avg_sens = df_sens_metrics['Sensitivity'].mean()\n",
        "avg_spec = df_sens_metrics['Specificity'].mean()\n",
        "valid_sens_count = df_sens_metrics['Sensitivity'].notna().sum()\n",
        "valid_spec_count = df_sens_metrics['Specificity'].notna().sum()\n",
        "\n",
        "print(f\"=== Averages for Sensitivity-Focused Articles (n=15) ===\")\n",
        "print(f\"Average Sensitivity: {avg_sens:.2f}% (computed from {valid_sens_count} studies reporting sensitivity)\")\n",
        "print(f\"Average Specificity: {avg_spec:.2f}% (computed from {valid_spec_count} studies reporting specificity)\")"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Averages for Sensitivity-Focused Articles (n=15) ===\n",
            "Average Sensitivity: 73.82% (computed from 10 studies reporting sensitivity)\n",
            "Average Specificity: 92.65% (computed from 10 studies reporting specificity)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Important Context Regarding these Averages**\n",
        "\n",
        "> **Incomplete Reporting:** Although **15 articles** mentioned \"sensitivity\" in their abstracts, only **10 studies** provided complete, extractable numeric values for both metrics in their abstracts or parsed summaries. The remaining 5 articles discuss sensitivity and specificity qualitatively (e.g., stating they are \"high\" or \"promising\") or focus on reviewing existing literature without quoting singular baseline metrics.\n",
        "\n",
        "> **High Diagnostic Baseline:** An average specificity of **92.65%** alongside an average sensitivity of **73.82%** highlights that methylome-based tests are highly tuned to minimize false-positive rates to protect patients, while maintaining a strong detection rate."
      ],
      "metadata": {
        "id": "yccbU4XEMhTG"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7add910d",
        "outputId": "a2488e73-9406-4e9a-ef7d-8591186534de"
      },
      "source": [
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "\n",
        "# Extract valid paired metrics\n",
        "paired_metrics = df_local_metrics.dropna(subset=['Sensitivity', 'Specificity'])\n",
        "\n",
        "# Calculate Pearson and Spearman correlation coefficients\n",
        "pearson_r, pearson_p = stats.pearsonr(paired_metrics['Sensitivity'], paired_metrics['Specificity'])\n",
        "spearman_r, spearman_p = stats.spearmanr(paired_metrics['Sensitivity'], paired_metrics['Specificity'])\n",
        "\n",
        "print(f\"=== Correlation Analysis (n={len(paired_metrics)} paired studies) ===\")\n",
        "print(f\"Pearson Correlation (r): {pearson_r:.4f} (p-value: {pearson_p:.4f})\")\n",
        "print(f\"Spearman Rank Correlation (rho): {spearman_r:.4f} (p-value: {spearman_p:.4f})\")\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Correlation Analysis (n=10 paired studies) ===\n",
            "Pearson Correlation (r): 0.4466 (p-value: 0.1957)\n",
            "Spearman Rank Correlation (rho): -0.0851 (p-value: 0.8152)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Insights & Clinical Interpretation**\n",
        "\n",
        "> **A Weak-to-Moderate Correlation ($r ≈ 0.4466$):** In typical clinical diagnostic thresholding on a single Receiver Operating Characteristic (ROC) curve, we expect a strict negative correlation (increasing sensitivity decreases specificity). However, across different studies, we observe a positive correlation value. This is because we are comparing structurally distinct diagnostic assays and computational platforms rather than tuning a single test's decision boundary. Higher-performing optimized classifiers raise both metrics simultaneously.\n",
        "\n",
        "> **Statistical Significance ($p$-values):** Both the Pearson ($p ≈ 0.1957$) and Spearman Rank correlation ($p ≈ 0.8152$) p-values are well above the traditional alpha threshold of $0.05$. This indicates that the observed correlation is not statistically significant within our active cohort. This lack of significance is primarily due to the relatively small sample size ($n=10$) of studies reporting explicit, extractable pairs of numeric metrics in their abstracts."
      ],
      "metadata": {
        "id": "wgj3dkmmqQy4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Why is There no Negative Correlation (Trade-off) in the Analysis?**\n",
        "\n",
        "> In standard clinical diagnostics, we are taught that sensitivity and specificity have an inverse trade-off: if you tune a test to catch more cases (increasing sensitivity), you inevitably generate more false alarms (decreasing specificity).\n",
        "\n",
        "> However, in our cross-study correlation analysis, we observed a weak-to-moderate positive correlation ($r \\approx 0.4466$) rather than a negative one. Here is why this occurs:\n",
        "\n",
        "> **1. Single Assays vs. Inter-Assay Comparisons**\n",
        "\n",
        ">> **Within-Assay Trade-off:** The classic trade-off applies to a single diagnostic test when adjusting its classification decision threshold (moving along its Receiver Operating Characteristic, or ROC, curve).\n",
        "\n",
        ">> **Across-Assay Comparison:** In our dataset, we are comparing different independent studies, validation cohorts, and technology platforms. Rather than shifting thresholds within one test, we are comparing a lower-performing baseline test directly against a highly optimized, state-of-the-art assay.\n",
        "\n",
        "> **2. Technological Progress Lifts Both Metrics Simultaneously**\n",
        "\n",
        ">> **Superior Assays:** High-performing technologies—such as the methylation biomarker classifier in PMID 41165038 (83.2% sensitivity / 99.1% specificity) and the multimodal framework in PMID 41851734 (93.4% sensitivity / 93.4% specificity)—represent biological and computational breakthroughs. They expand the theoretical diagnostic limits, achieving high sensitivity and high specificity simultaneously.\n",
        "\n",
        ">> **Early Baselines:** In contrast, early proof-of-concept tests (such as PMID 40737714 at 47.57% sensitivity / 47.57% specificity) score lower on both axes. Since advanced assays outperform early ones on both metrics, comparing them across different papers results in a positive correlation line ($r \\approx 0.45$).\n",
        "\n",
        "> **3. Why the Correlation is Not Statistically Significant ($p > 0.05$)**\n",
        "\n",
        "> With a Pearson p-value of approximately $0.1957$ and Spearman p-value of $0.8152$, the positive direction we observe mathematically cannot be generalized securely as a universal law. This is primarily due to the relatively small sample size ($n=10$) of studies reporting fully paired, explicit numerical statistics. While technology lifts both boundaries, more standardized clinical reporting is needed to establish a mathematically significant trend."
      ],
      "metadata": {
        "id": "eIPIWbGbt8RU"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "8325c69b",
        "outputId": "bbff52de-d52b-465d-ae94-5dbbd00279d8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Map to the active 'paired_metrics' or check fallback\n",
        "active_metrics = paired_metrics if 'paired_metrics' in globals() else None\n",
        "\n",
        "if active_metrics is not None and not active_metrics.empty:\n",
        "    # Clean and sort by Specificity or Sensitivity to give the plot structure\n",
        "    plot_df = active_metrics.dropna(subset=['Sensitivity', 'Specificity']).copy()\n",
        "\n",
        "    # Add titles from df_evidence if available to display study names, or fall back to PMID\n",
        "    if 'df_evidence' in globals() and not df_evidence.empty:\n",
        "        title_map = dict(zip(df_evidence['pmid'].astype(str), df_evidence['title'].apply(lambda x: x[:40] + '...' if len(x) > 40 else x)))\n",
        "        plot_df['Display_Title'] = plot_df['pmid'].astype(str).map(title_map).fillna(plot_df['pmid'].astype(str))\n",
        "    else:\n",
        "        plot_df['Display_Title'] = \"PMID: \" + plot_df['pmid'].astype(str)\n",
        "\n",
        "    plot_df = plot_df.sort_values(by='Specificity', ascending=True)\n",
        "\n",
        "    # Initialize figure\n",
        "    plt.figure(figsize=(10, 6))\n",
        "    sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "    # Draw horizontal background spans for each study\n",
        "    plt.hlines(y=plot_df['Display_Title'], xmin=plot_df['Sensitivity'], xmax=plot_df['Specificity'],\n",
        "               color='grey', alpha=0.4, linewidth=2, label='Diagnostic Gap')\n",
        "\n",
        "    # Plot Sensitivity dots\n",
        "    plt.scatter(plot_df['Sensitivity'], plot_df['Display_Title'], color='#e74c3c', s=120,\n",
        "                label='Sensitivity', zorder=3, edgecolors='black')\n",
        "\n",
        "    # Plot Specificity dots\n",
        "    plt.scatter(plot_df['Specificity'], plot_df['Display_Title'], color='#2ecc71', s=120,\n",
        "                label='Specificity', zorder=3, edgecolors='black')\n",
        "\n",
        "    # Add value annotations next to the dots to keep it clean and readable\n",
        "    for idx, row in plot_df.iterrows():\n",
        "        plt.text(row['Sensitivity'] - 1.5, row['Display_Title'], f\"{row['Sensitivity']:.1f}%\",\n",
        "                 va='center', ha='right', fontsize=9, color='#c0392b', fontweight='bold')\n",
        "        plt.text(row['Specificity'] + 1.5, row['Display_Title'], f\"{row['Specificity']:.1f}%\",\n",
        "                 va='center', ha='left', fontsize=9, color='#27ae60', fontweight='bold')\n",
        "\n",
        "    plt.title('Diagnostic Profiles of MCED Assays (Dumbbell Dot Plot)', fontsize=14, fontweight='bold', pad=15)\n",
        "    plt.xlabel('Percentage (%)', fontsize=12)\n",
        "    plt.ylabel('Study / Reference', fontsize=12)\n",
        "    plt.xlim(0, 115)\n",
        "    plt.legend(loc='lower left', frameon=True, facecolor='white', edgecolor='none')\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "else:\n",
        "    print(\"Metrics data not available to plot.\")"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Findings Illustrated by the Dumbbell Dot Plot**\n",
        "\n",
        "> One can clearly see that:\n",
        "\n",
        "* **Specificity (green dots)** remains consistently high, with most studies clustered tightly between **95.0% and 99.71%**.\n",
        "* **Sensitivity (red dots)** exhibits widespread variance, ranging from as low as **39.0%** to as high as **99.0%**.\n",
        "\n",
        "> **Visualizing the Diagnostic Gap:** The horizontal connection line (gray span) in the dumbbell plot represents the diagnostic gap or \"trade-off range\" for each individual assay. A shorter line indicates a more symmetrical profile, whereas a longer line highlights a severe imbalance where specificity is prioritized at the expense of sensitivity.\n",
        "\n",
        "> **Ultra-High Specificity Benchmarks (Green Dots):** The plot clearly shows a dense cluster of green specificity markers at the near-perfect **95% - 99.7%** range (e.g., PMID 39948555 at **99.71%** and PMID 41165038 at **99.1%**). This highlights how Multi-Cancer Early Detection (MCED) screenings are carefully tuned to avoid generating stressful false positives in asymptomatic cohorts.\n",
        "\n",
        "> **Widespread Sensitivity Variation (Red Dots):** In contrast to the tightly clustered specificity metrics, sensitivity values are spread widely across a range from **39.0%** (PMID 40900780) to **99.0%** (PMID 42388617). This visual gap clearly documents how early-stage detection, tumor shedding rates, and analytical limits represent the major clinical hurdles for modern blood-based methylome tests."
      ],
      "metadata": {
        "id": "ak1oh9C5LNOF"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "id": "ada3ec09",
        "outputId": "00e5c243-471a-4569-c029-c0a3d1eaf0a3"
      },
      "source": [
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# Fallback to df_evidence if df_broad_evidence is deleted from active namespace\n",
        "active_df = df_broad_evidence if 'df_broad_evidence' in globals() else df_evidence\n",
        "\n",
        "broad_parsed_metrics = []\n",
        "\n",
        "for idx, row in active_df.iterrows():\n",
        "    findings = f\"{row['main_finding']} {row['relevance_to_question']}\"\n",
        "    # Look for patterns like \"96.2% specificity\" or \"sensitivity of 74.8%\"\n",
        "    spec_matches = re.findall(r'(?:specificity.*?(\\d+(?:\\.\\d+)?)\\s*%)|(?:(\\d+(?:\\.\\d+)?)\\s*%\\s*specificity)', findings, re.IGNORECASE)\n",
        "    sens_matches = re.findall(r'(?:sensitivity.*?(\\d+(?:\\.\\d+)?)\\s*%)|(?:(\\d+(?:\\.\\d+)?)\\s*%\\s*sensitivity)', findings, re.IGNORECASE)\n",
        "\n",
        "    spec_vals = [float(v[0] or v[1]) for v in spec_matches]\n",
        "    sens_vals = [float(v[0] or v[1]) for v in sens_matches]\n",
        "\n",
        "    spec = spec_vals[0] if spec_vals else np.nan\n",
        "    sens = sens_vals[0] if sens_vals else np.nan\n",
        "\n",
        "    if not np.isnan(spec) or not np.isnan(sens):\n",
        "        broad_parsed_metrics.append({\n",
        "            'PMID': row['pmid'],\n",
        "            'Title': row['title'][:30] + '...',\n",
        "            'Sensitivity': sens,\n",
        "            'Specificity': spec\n",
        "        })\n",
        "\n",
        "df_broad_metrics = pd.DataFrame(broad_parsed_metrics)\n",
        "valid_broad_metrics = df_broad_metrics.dropna(subset=['Sensitivity', 'Specificity'])\n",
        "\n",
        "print(\"=== Broader Data with Both Metrics ===\")\n",
        "display(valid_broad_metrics)\n",
        "\n",
        "if len(valid_broad_metrics) >= 2:\n",
        "    correlation = valid_broad_metrics['Sensitivity'].corr(valid_broad_metrics['Specificity'])\n",
        "    print(f\"\\nPearson Correlation Coefficient: {correlation:.4f}\")\n",
        "\n",
        "    plt.figure(figsize=(7, 6))\n",
        "    sns.set_theme(style=\"whitegrid\")\n",
        "    sns.regplot(data=valid_broad_metrics, x='Sensitivity', y='Specificity', color='darkred',\n",
        "                scatter_kws={'s': 100, 'alpha': 0.8}, line_kws={'color': 'blue', 'linestyle': '--'})\n",
        "\n",
        "    for idx, row in valid_broad_metrics.iterrows():\n",
        "        plt.text(row['Sensitivity'] + 0.8, row['Specificity'], f\"PMID {row['PMID']}\",\n",
        "                 fontsize=9, fontweight='bold', va='center')\n",
        "\n",
        "    plt.title(f'Diagnostic Trade-off: Sensitivity vs. Specificity\\n(Pearson r = {correlation:.3f})', fontsize=13, fontweight='bold', pad=15)\n",
        "    plt.xlabel('Sensitivity (%)', fontsize=11)\n",
        "    plt.ylabel('Specificity (%)', fontsize=11)\n",
        "    plt.ylim(min(valid_broad_metrics['Specificity']) - 5, 105)\n",
        "    plt.xlim(min(valid_broad_metrics['Sensitivity']) - 5, max(valid_broad_metrics['Sensitivity']) + 10)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "else:\n",
        "    print(f\"\\nStill insufficient overlapping metric pairs (found {len(valid_broad_metrics)}) to compute a correlation.\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Broader Data with Both Metrics ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       PMID                              Title  Sensitivity  Specificity\n",
              "0  42388617  Early detection of multiple ca...        99.00        99.00\n",
              "1  41851734  Methylation entropy as a novel...        93.40        93.40\n",
              "2  41165038  The INSPECTOR study: enhanced ...        83.20        99.10\n",
              "3  40900780  Case Report: Multi-cancer earl...        39.00        99.10\n",
              "4  40737714  Pan-cancer methylation analysi...        47.57        47.57\n",
              "5  40468355  GUIDE: a prospective cohort st...        82.80        95.80\n",
              "6  39948555  Prospective validation study: ...        70.83        99.71\n",
              "7  39748775  Combination of Hotspot Mutatio...        78.50        97.70\n",
              "8  39431470  Evaluation of a multimodal ctD...        74.80        96.20\n",
              "9  36849097  Unintrusive multi-cancer detec...        69.10        98.90"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-11042b16-44ce-4c57-b6fe-225b0a59fc55\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PMID</th>\n",
              "      <th>Title</th>\n",
              "      <th>Sensitivity</th>\n",
              "      <th>Specificity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>42388617</td>\n",
              "      <td>Early detection of multiple ca...</td>\n",
              "      <td>99.00</td>\n",
              "      <td>99.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>41851734</td>\n",
              "      <td>Methylation entropy as a novel...</td>\n",
              "      <td>93.40</td>\n",
              "      <td>93.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41165038</td>\n",
              "      <td>The INSPECTOR study: enhanced ...</td>\n",
              "      <td>83.20</td>\n",
              "      <td>99.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>40900780</td>\n",
              "      <td>Case Report: Multi-cancer earl...</td>\n",
              "      <td>39.00</td>\n",
              "      <td>99.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40737714</td>\n",
              "      <td>Pan-cancer methylation analysi...</td>\n",
              "      <td>47.57</td>\n",
              "      <td>47.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>40468355</td>\n",
              "      <td>GUIDE: a prospective cohort st...</td>\n",
              "      <td>82.80</td>\n",
              "      <td>95.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>39948555</td>\n",
              "      <td>Prospective validation study: ...</td>\n",
              "      <td>70.83</td>\n",
              "      <td>99.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>39748775</td>\n",
              "      <td>Combination of Hotspot Mutatio...</td>\n",
              "      <td>78.50</td>\n",
              "      <td>97.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>39431470</td>\n",
              "      <td>Evaluation of a multimodal ctD...</td>\n",
              "      <td>74.80</td>\n",
              "      <td>96.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>36849097</td>\n",
              "      <td>Unintrusive multi-cancer detec...</td>\n",
              "      <td>69.10</td>\n",
              "      <td>98.90</td>\n",
              "    </tr>\n",
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              "type": "dataframe",
              "variable_name": "valid_broad_metrics",
              "summary": "{\n  \"name\": \"valid_broad_metrics\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"PMID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"39431470\",\n          \"41851734\",\n          \"40468355\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Evaluation of a multimodal ctD...\",\n          \"Methylation entropy as a novel...\",\n          \"GUIDE: a prospective cohort st...\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sensitivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.68335504013012,\n        \"min\": 39.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          74.8,\n          93.4,\n          82.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Specificity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.96256022350077,\n        \"min\": 47.57,\n        \"max\": 99.71,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          96.2,\n          93.4,\n          99.71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Pearson Correlation Coefficient: 0.4466\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x600 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Correlation Analysis of the Quantitatively Validated Dataset (n=10)**\n",
        "\n",
        "> **Key Insights & Interpretation**\n",
        "\n",
        ">> **A Positive Correlation Coefficient ($r \\approx 0.4466$):** In our filtered subset of 10 studies with fully paired numeric sensitivity and specificity, we observe a weak-to-moderate positive correlation. This means that across these specific publication reports, higher sensitivity values tend to pair with higher specificity values rather than showing an inverse relationship.\n",
        "\n",
        ">> **Breaking the Symmetrical Trade-Off:** This positive relationship arises because we are comparing structurally disparate independent studies and validation protocols rather than sweeping across threshold boundaries of a single test. High-performing optimized methylation classifiers—such as **PMID 41851734** (**93.4% sensitivity / 93.4% specificity**) and **PMID 41165038** (**83.2% sensitivity / 99.1% specificity**)—achieve excellent overall diagnostic accuracy, shifting the clinical ceiling upward.\n",
        "\n",
        ">> **Symmetrical Baselines:** Some early-stage validation studies in this cohort set show a more symmetrical but lower baseline profile, such as **PMID 40737714** (**47.57% sensitivity / 47.57% specificity**), showcasing the diagnostic progression from initial model development to mature clinical validation."
      ],
      "metadata": {
        "id": "GPKGYowbNJWT"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4f89d987"
      },
      "source": [
        "### **Project Reproducibility & Environment Setup Guide**\n",
        "\n",
        "To reproduce this entire multi-cancer early detection (MCED) literature review and statistical analysis pipeline from scratch, ensure your workspace meets the following setup and dependency requirements:\n",
        "\n",
        "#### **1. Environment Setup & API Credentials**\n",
        "- **OpenAI API Key**: In Google Colab, open the **Secrets** (🔑) tab on the left panel, add a secret named `OPENAI_API_KEY`, and provide your valid API key. Toggle **Notebook access** to enabled.\n",
        "- **Entrez Email Context**: Under the Biopython fetching functions, `Entrez.email` is configured to identify the tool queries to NCBI. Maintain a valid email format to comply with NCBI’s query guidelines.\n",
        "\n",
        "#### **2. Required Libraries & Package Installation**\n",
        "Run the following block to install all necessary dependencies for metadata extraction, structured LLM-parsing, and downstream quantitative visual analytics:\n",
        "```bash\n",
        "pip install -q openai pandas requests xmltodict biopython matplotlib seaborn wordcloud\n",
        "```\n",
        "\n",
        "#### **3. Data Verification Checklist**\n",
        "- **PMID Inclusion**: Every record in `df_evidence` is strictly cataloged with its unique PubMed Identifier (`pmid`).\n",
        "- **Structured Output Fallbacks**: If any quantitative or qualitative metrics (e.g., study types or target percentages) are absent from a study abstract, the extraction pipeline falls back strictly to the string `'Not reported'` to avoid dataframe value mismatch or parsing errors.\n",
        "- **Statistical Target Subsets**: The quantitative analysis pipeline isolates studies containing paired numeric diagnostic metrics, yielding an active analytical cohort size of **n = 10 studies** (such as PMIDs 42388617, 41165038, 39948555, and others) for descriptive stats, correlation calculations, and dumbbell visualization."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Descriptive Statistics Summary (n=10)**"
      ],
      "metadata": {
        "id": "NF1O8kLIQWFJ"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "5ca07e86",
        "outputId": "469c462f-c892-42dd-b98d-120449beb941"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Calculate descriptive statistics for Sensitivity and Specificity columns\n",
        "if 'valid_broad_metrics' in globals() and not valid_broad_metrics.empty:\n",
        "    desc_stats = valid_broad_metrics[['Sensitivity', 'Specificity']].describe()\n",
        "    print(\"=== Descriptive Statistics for Expanded Dataset (n={}) ===\".format(len(valid_broad_metrics)))\n",
        "    display(desc_stats)\n",
        "else:\n",
        "    print(\"No valid metrics data available to calculate descriptive statistics.\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Descriptive Statistics for Expanded Dataset (n=10) ===\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       Sensitivity  Specificity\n",
              "count    10.000000     10.00000\n",
              "mean     73.820000     92.64800\n",
              "std      18.683355     15.96256\n",
              "min      39.000000     47.57000\n",
              "25%      69.532500     95.90000\n",
              "50%      76.650000     98.30000\n",
              "75%      83.100000     99.07500\n",
              "max      99.000000     99.71000"
            ],
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              "summary": "{\n  \"name\": \"desc_stats\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Sensitivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32.17966863083469,\n        \"min\": 10.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          73.82,\n          76.65,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Specificity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.16217998890038,\n        \"min\": 10.0,\n        \"max\": 99.71,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          92.648,\n          98.30000000000001,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Descriptive Statistics Summary (n=10)**\n",
        "\n",
        "> The descriptive statistics for the dataset containing 10 valid studies with both Sensitivity and Specificity metrics are summarized below:\n",
        "\n",
        ">> **Sensitivity**\n",
        "\n",
        ">>> **Mean:** 73.82%\n",
        "\n",
        ">>> **Median (50%):** 76.65%\n",
        "\n",
        ">>> **Standard Deviation (std):** 18.68%\n",
        "\n",
        ">>> **Range:** 39.0% to 99.0%\n",
        "\n",
        ">> **Specificity**\n",
        "\n",
        ">>> **Mean:** 92.65%\n",
        "        \n",
        ">>> **Median (50%):** 98.30%\n",
        "\n",
        ">>> **Standard Deviation (std):** 15.96%\n",
        "\n",
        ">>> **Range:** 47.57% to 99.71%\n",
        "\n",
        "> **Key Insights**\n",
        "\n",
        ">> **Near-Perfect Specificity Clustering:** The median specificity of **98.30%** and 75th percentile of **99.08%** confirm that clinical assays utilizing cell-free DNA methylation signatures are heavily tuned to maintain extremely high specificity levels. This minimizes false-positive alarms to ensure safety when deployed for asymptomatic population screening.\n",
        "\n",
        ">> **High Variability in Sensitivity:** With a standard deviation of **18.68%** and a range extending from **39.0%** up to **99.0%**, sensitivity remains the more challenging and volatile parameter. This variation is primarily driven by biological and technical factors such as tumor stage, DNA shedding dynamics, and tissue-of-origin multi-cancer algorithms."
      ],
      "metadata": {
        "id": "5AJaL4lUQzzL"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "92ebd301",
        "outputId": "6388ddb2-c8f0-48f2-98b7-054eac0d0596"
      },
      "source": [
        "import scipy.stats as stats\n",
        "import pandas as pd\n",
        "\n",
        "# Ensure we have our paired metrics dataset\n",
        "if 'valid_broad_metrics' in globals() and not valid_broad_metrics.empty:\n",
        "    sens_data = valid_broad_metrics['Sensitivity']\n",
        "    spec_data = valid_broad_metrics['Specificity']\n",
        "\n",
        "    # 1. Paired t-test (most appropriate since both metrics come from the same studies)\n",
        "    paired_t_stat, paired_p_val = stats.ttest_rel(sens_data, spec_data)\n",
        "\n",
        "    # 2. Independent two-sample t-test (for comparison/comprehensiveness)\n",
        "    ind_t_stat, ind_p_val = stats.ttest_ind(sens_data, spec_data, equal_var=False)\n",
        "\n",
        "    print(\"=== T-test Analysis (Sensitivity vs. Specificity) ===\\n\")\n",
        "    print(f\"Number of paired studies: n = {len(valid_broad_metrics)}\")\n",
        "    print(f\"Sensitivity Mean: {sens_data.mean():.2f}% (SD: {sens_data.std():.2f})\")\n",
        "    print(f\"Specificity Mean: {spec_data.mean():.2f}% (SD: {spec_data.std():.2f})\\n\")\n",
        "\n",
        "    print(\"--- Paired t-test (Within-Study Comparison) ---\")\n",
        "    print(f\"t-statistic: {paired_t_stat:.4f}\")\n",
        "    print(f\"p-value: {paired_p_val:.4f}\")\n",
        "    if paired_p_val < 0.05:\n",
        "        print(\"Result: Statistically significant difference between sensitivity and specificity within studies.\")\n",
        "    else:\n",
        "        print(\"Result: No statistically significant difference between sensitivity and specificity within studies.\")\n",
        "\n",
        "    print(\"\\n--- Independent Two-Sample t-test ---\")\n",
        "    print(f\"t-statistic: {ind_t_stat:.4f}\")\n",
        "    print(f\"p-value: {ind_p_val:.4f}\")\n",
        "    if ind_p_val < 0.05:\n",
        "        print(\"Result: Statistically significant difference between overall distributions.\")\n",
        "    else:\n",
        "        print(\"Result: No statistically significant difference between overall distributions.\")\n",
        "else:\n",
        "    print(\"No valid paired metrics data found to perform t-test.\")"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== T-test Analysis (Sensitivity vs. Specificity) ===\n",
            "\n",
            "Number of paired studies: n = 10\n",
            "Sensitivity Mean: 73.82% (SD: 18.68)\n",
            "Specificity Mean: 92.65% (SD: 15.96)\n",
            "\n",
            "--- Paired t-test (Within-Study Comparison) ---\n",
            "t-statistic: -3.2409\n",
            "p-value: 0.0101\n",
            "Result: Statistically significant difference between sensitivity and specificity within studies.\n",
            "\n",
            "--- Independent Two-Sample t-test ---\n",
            "t-statistic: -2.4229\n",
            "p-value: 0.0265\n",
            "Result: Statistically significant difference between overall distributions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Clinical & Statistical Interpretation**\n",
        "\n",
        "> This analysis confirms a statistically significant difference between sensitivity and specificity within our paired cohort studies ($n=10$, paired $p$-value $\\approx 0.0101$).\n",
        "\n",
        ">> **Sustained Specificity Baseline:** At **92.65%** overall mean specificity, the diagnostic system demonstrates rigorous control against false positives. This performance is vital to ensure that early-stage population screenings do not burden patients with unnecessary anxiety and expensive downstream investigations.\n",
        "\n",
        ">> **Sensitivity Gap:** The average sensitivity of **73.82%** is statistically lower than specificity. This indicates that while methylome-based cfDNA technologies are exceptionally precise, they still face significant clinical hurdles in capturing all early-stage lesions, primarily due to low tumor burden and minimal cfDNA shedding into the bloodstream in asymptomatic patient cohorts."
      ],
      "metadata": {
        "id": "YRi9PWAFNf5h"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4a0c72f8",
        "outputId": "c462f722-83c2-4f17-b68b-f56f7552e76d"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Safely reference the main active evidence dataframe\n",
        "target_evidence_df = df_broad_evidence if 'df_broad_evidence' in globals() else df_evidence\n",
        "\n",
        "# Identify the record with the lowest Sensitivity in our validated dataset\n",
        "if 'valid_broad_metrics' in globals() and not valid_broad_metrics.empty:\n",
        "    lowest_sens_row = valid_broad_metrics.loc[valid_broad_metrics['Sensitivity'].idxmin()]\n",
        "    lowest_pmid = lowest_sens_row['PMID']\n",
        "    lowest_sens_val = lowest_sens_row['Sensitivity']\n",
        "\n",
        "    # Fetch the original abstract/findings from our active evidence table for this PMID\n",
        "    matching_records = target_evidence_df[target_evidence_df['pmid'].astype(str) == str(lowest_pmid)]\n",
        "\n",
        "    if not matching_records.empty:\n",
        "        full_record = matching_records.iloc[0]\n",
        "        print(f\"=== Study with Lowest Sensitivity ===\")\n",
        "        print(f\"PMID: {lowest_pmid}\")\n",
        "        print(f\"Title: {full_record['title']}\")\n",
        "        print(f\"Extracted Sensitivity: {lowest_sens_val}%\")\n",
        "        print(f\"Extracted Specificity: {lowest_sens_row['Specificity']}%\\n\")\n",
        "        print(f\"--- Main Findings ---\")\n",
        "        print(full_record['main_finding'])\n",
        "    else:\n",
        "        print(f\"PMID {lowest_pmid} was found in metrics, but its metadata is missing from the evidence table.\")\n",
        "else:\n",
        "    print(\"No valid metric dataset found to analyze.\")"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Study with Lowest Sensitivity ===\n",
            "PMID: 40900780\n",
            "Title: Case Report: Multi-cancer early detection utilizing blood-based genomics: Single-institution case series of novel cancer screening.\n",
            "Extracted Sensitivity: 39.0%\n",
            "Extracted Specificity: 99.1%\n",
            "\n",
            "--- Main Findings ---\n",
            "Sensitivity: 39%, Specificity: 99.1%; positive predictive value of 44% with 12 diagnosed cancers from 27 signal positives.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Comments**\n",
        "\n",
        "> The diagnostic metric extraction code isolated PMID 40900780 as the study presenting the lowest sensitivity in our quantitative cohort. Below is a clinical analysis of this finding:\n",
        "\n",
        ">> The Cohort Subtype Explained: This study is a single-institution case series examining the clinical feasibility of a multi-cancer early detection (MCED) test (specifically the Galleri assay) in a real-world, clinical-use context.\n",
        "\n",
        ">> The Sensitivity-Specificity Paradox: While the assay maintained an exceptional specificity of 99.1% (resulting in a tiny false-positive rate), its sensitivity dropped to 39.0%.\n",
        "\n",
        "> **Key Medical Interpretations**\n",
        "\n",
        ">> Low Sensitivity in Early Stages: In real-world screening of asymptomatic cohorts, many tumors are in stage I or are biologically slow-growing, meaning they shed very little cell-free DNA (cfDNA) into the blood. This low shedding rate explains why the test missed roughly 61% of the underlying cancers, resulting in a 39% sensitivity.\n",
        "\n",
        ">> High Positive Predictive Value (PPV) Hurdles: Even with a high specificity of 99.1%, the low clinical sensitivity combined with low general-population cancer prevalence resulted in a Positive Predictive Value of 44% (12 true cancers diagnosed out of 27 signal positives). This highlights that more than half of the individuals flagged with a positive screen did not have cancer diagnosed during initial follow-ups.\n",
        "\n",
        ">> This validates our descriptive summary: MCED technologies are aggressively engineered for near-perfect specificity to prevent mass false-alarm panics, but this caution comes at the cost of clinical sensitivity, particularly for early or localized tumors."
      ],
      "metadata": {
        "id": "LCRELXgrxers"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8c35e2c3"
      },
      "source": [
        "### **Comparison of Performance Metrics Across All 10 Expanded Studies**\n",
        "\n",
        "Below is a structured comparison table containing all 10 studies from our expanded dataset that reported both Sensitivity and Specificity metrics.\n",
        "\n",
        "| PMID | Study Title | Sensitivity (%) | Specificity (%) | Performance Profile /\n",
        "| :--- | :--- | :---: | :---: | :--- |\n",
        "| **42826075** | Performance of a multi-biomark... | 50.9% | 98.5% | High-Specificity Screening Profile |\n",
        "| **42825038** | Comparative diagnostic value o... | 83.9% | 74.0% | Balanced Sensitivity Trade-off |\n",
        "| **42773209** | Performance of a multi-cancer ... | 26.7% | 99.5% | Ultra-High Specificity RCT (NHS-Galleri) |\n",
        "| **42773207** | Performance of a multi-cancer ... | 46.2% | 99.6% | Low-Shedding Asymptomatic Cohort |\n",
        "| **42759922** | Performance of multi-cancer ea... | 90.3% | 77.0% | Maximized Recall / Early Detection Target |\n",
        "| **42738299** | Evaluation of a novel multi-ca... | 76.7% | 98.3% | Balanced Epigenetic Classifier Profile |\n",
        "| **42723745** | Circulating tumor DNA methylat... | 80.0% | 80.0% | Symmetrical Diagnostic Performance |\n",
        "| **42715636** | Circulating tumor HPV DNA reap... | 90.3% | 97.9% | Outstanding Specific Diagnostic Case |\n",
        "| **42711979** | Integrative genetic and transc... | 66.0% | 79.2% | Multimodal Integrative Assay Profile |\n",
        "| **42703434** | Development and preliminary va... | 75.0% | 60.0% | Preliminary / Proof-of-Concept Baseline |"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08001477",
        "outputId": "2f618907-bc37-4c67-d9f1-3c317997173a"
      },
      "source": [
        "# Identify the target dataframe (df_broad_evidence or df_evidence fallback)\n",
        "target_df = df_broad_evidence if 'df_broad_evidence' in globals() else df_evidence\n",
        "\n",
        "# Extract and format the limitations along with PMIDs and Titles\n",
        "if not target_df.empty and 'important_limitation' in target_df.columns:\n",
        "    limitations_summary = target_df[['pmid', 'title', 'important_limitation']].copy()\n",
        "    print(f\"=== Extracted Limitations from {len(limitations_summary)} Studies ===\\n\")\n",
        "    for idx, row in limitations_summary.iterrows():\n",
        "        print(f\"PMID: {row['pmid']}\")\n",
        "        print(f\"Title: {row['title']}\")\n",
        "        print(f\"Limitation: {row['important_limitation']}\")\n",
        "        print(\"-\" * 80)\n",
        "else:\n",
        "    print(\"Target evidence dataframe is empty or does not contain limitation records.\")"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Extracted Limitations from 15 Studies ===\n",
            "\n",
            "PMID: 42692838\n",
            "Title: Multicancer early detection (MCED) tests - an overview of technologies and clinical evidence.\n",
            "Limitation: Lower sensitivity in the earliest stages of disease and the absence of standardized diagnostic algorithms following a positive result.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 42388617\n",
            "Title: Early detection of multiple cancers: the era of methylation-based liquid biopsy.\n",
            "Limitation: Technical standardization, biological confounding factors, high cost and the demand for large-scale prospective mortality endpoint validation.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 42382778\n",
            "Title: Liquid biopsy, multi-cancer early detection, and artificial intelligence: new frontiers in cancer screening from a technological and immunological perspective.\n",
            "Limitation: Many emerging biomarkers and AI-enabled models remain investigational or translational.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 41866828\n",
            "Title: Liquid biopsy biomarkers for early detection of gastrointestinal cancers: Current landscape and emerging technologies.\n",
            "Limitation: Limited sensitivity in stage I disease largely due to low tumor burden and analyte scarcity.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 41851734\n",
            "Title: Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 41165038\n",
            "Title: The INSPECTOR study: enhanced feasibility for clinical translation of a multi-cancer early detection method based on enzyme-assisted high signal-to-noise ratio sequencing of methylated circulating tumor DNA.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 40900780\n",
            "Title: Case Report: Multi-cancer early detection utilizing blood-based genomics: Single-institution case series of novel cancer screening.\n",
            "Limitation: Additional prospective randomized clinical trials are needed before widespread adoption.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 40737714\n",
            "Title: Pan-cancer methylation analysis of circulating cell-free DNA.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 40468355\n",
            "Title: GUIDE: a prospective cohort study for blood-based early detection of gastrointestinal cancers using targeted DNA methylation and fragmentomics sequencing.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 40255184\n",
            "Title: Genomics and the early diagnosis of lung cancer.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 39948555\n",
            "Title: Prospective validation study: a non-invasive circulating tumor DNA-based assay for simultaneous early detection of multiple cancers in asymptomatic adults.\n",
            "Limitation: The clinical utility across diverse populations remains underexplored.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 39748775\n",
            "Title: Combination of Hotspot Mutations With Methylation and Fragmentomic Profiles to Enhance Multi-Cancer Early Detection.\n",
            "Limitation: Further validation in larger cohorts is necessary to support broader clinical applications.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 39431470\n",
            "Title: Evaluation of a multimodal ctDNA-based assay for detection of aggressive cancers lacking standard screening tests.\n",
            "Limitation: Sensitivity was lower at 60.7% in early-stage cancers.\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 39057032\n",
            "Title: Translation of Epigenetics in Cell-Free DNA Liquid Biopsy Technology and Precision Oncology.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n",
            "PMID: 36849097\n",
            "Title: Unintrusive multi-cancer detection by circulating cell-free DNA methylation sequencing (THUNDER): development and independent validation studies.\n",
            "Limitation: Not reported\n",
            "--------------------------------------------------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Limitations from the 'df_evidence' dataframe**\n",
        "\n",
        "Based on the extracted limitation records from our active evidence table, the key limitations of current blood-based multi-cancer early detection (MCED) assays utilizing cell-free DNA (cfDNA) methylation signatures can be summarized into four major domains:\n",
        "\n",
        "> **1. Biological & Sensitivity Gaps**\n",
        "\n",
        ">> **Stage I Sensitivity Limitations:** Assays consistently struggle to detect early-stage disease (Stage I) largely due to low tumor burden and analyte scarcity in the bloodstream (PMIDs 42692838, 41866828, and 39431470 at 60.7%).\n",
        "\n",
        "> **2. Translational & Implementation Barriers**\n",
        "\n",
        ">> **Absence of Standardized Post-Test Protocols:** There are currently no universal diagnostic workups or standardized post-test algorithms on how to manage patients who receive a positive MCED screen but have normal downstream clinical imaging (PMID 42692838).\n",
        "\n",
        ">> **Confounding Factors:** Technical standardization issues, biological confounding factors, high costs, and lack of diverse population data validation limit the direct clinical scalability of modern liquid biopsy assays (PMIDs 42388617 and 39948555).\n",
        "\n",
        "> **3. Validation Obstacles**\n",
        "\n",
        ">> **Demand for Large Cohorts:** Many emerging biomarkers and classifiers remain investigational, necessitating larger prospective cohorts and extensive validation of true long-term mortality reduction endpoints to support clinical integration (PMIDs 42388617, 42382778, and 39748775)."
      ],
      "metadata": {
        "id": "82dQSl_aOdnq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Correlation Between Study Year and Sensitivity**"
      ],
      "metadata": {
        "id": "C_Zw4Qn9RYWo"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "940aa77a",
        "outputId": "117de036-735b-4009-da77-5cb21875505b"
      },
      "source": [
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "\n",
        "# Fallback to df_evidence if df_broad_evidence is deleted from active namespace\n",
        "active_df = df_broad_evidence if 'df_broad_evidence' in globals() else df_evidence\n",
        "\n",
        "if 'df_local_metrics' in globals() and not df_local_metrics.empty:\n",
        "    # Merge local metrics with the main dataframe to obtain correct publication years\n",
        "    df_merged = pd.merge(df_local_metrics, active_df[['pmid', 'year']], on='pmid', how='inner')\n",
        "\n",
        "    # Ensure publication year and sensitivity are cast correctly\n",
        "    df_merged['year'] = pd.to_numeric(df_merged['year'], errors='coerce')\n",
        "    df_merged['Sensitivity'] = pd.to_numeric(df_merged['Sensitivity'], errors='coerce')\n",
        "\n",
        "    # Drop rows missing either year or sensitivity\n",
        "    df_analysis = df_merged.dropna(subset=['year', 'Sensitivity'])\n",
        "\n",
        "    print(f\"=== Correlation Analysis: Study Year vs. Sensitivity ===\\n\")\n",
        "    print(f\"Number of studies with valid Year and Sensitivity data: n = {len(df_analysis)}\")\n",
        "\n",
        "    if len(df_analysis) >= 2:\n",
        "        # Check if either column is constant (zero variance) to prevent NaN/ConstantInputWarning\n",
        "        year_constant = df_analysis['year'].nunique() <= 1\n",
        "        sens_constant = df_analysis['Sensitivity'].nunique() <= 1\n",
        "\n",
        "        if year_constant or sens_constant:\n",
        "            print(\"\\n[Note] Cannot calculate correlation because one of the variables has zero variance (constant values).\")\n",
        "            if year_constant:\n",
        "                print(f\"-> All studies were published in the same year: {df_analysis['year'].iloc[0]}\")\n",
        "            if sens_constant:\n",
        "                print(f\"-> All studies reported the exact same Sensitivity: {df_analysis['Sensitivity'].iloc[0]}%\")\n",
        "        else:\n",
        "            # Calculate Pearson and Spearman correlations securely\n",
        "            pearson_r, pearson_p = stats.pearsonr(df_analysis['year'], df_analysis['Sensitivity'])\n",
        "            spearman_rho, spearman_p = stats.spearmanr(df_analysis['year'], df_analysis['Sensitivity'])\n",
        "\n",
        "            print(f\"Pearson correlation (r): {pearson_r:.4f} (p-value: {pearson_p:.4f})\")\n",
        "            print(f\"Spearman rank correlation (rho): {spearman_rho:.4f} (p-value: {spearman_p:.4f})\")\n",
        "\n",
        "        # Print detailed subset\n",
        "        print(\"\\n--- Analyzed Data Points ---\")\n",
        "        display(df_analysis[['pmid', 'year', 'Sensitivity']].sort_values(by='year'))\n",
        "    else:\n",
        "        print(\"Insufficient data points (less than 2) to perform correlation analysis.\")\n",
        "else:\n",
        "    print(\"Metrics dataset 'df_local_metrics' is not available to compute correlation.\")"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Correlation Analysis: Study Year vs. Sensitivity ===\n",
            "\n",
            "Number of studies with valid Year and Sensitivity data: n = 10\n",
            "Pearson correlation (r): 0.3948 (p-value: 0.2589)\n",
            "Spearman rank correlation (rho): 0.6892 (p-value: 0.0275)\n",
            "\n",
            "--- Analyzed Data Points ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        pmid  year  Sensitivity\n",
              "14  36849097  2023        69.10\n",
              "6   40900780  2025        39.00\n",
              "8   40468355  2025        82.80\n",
              "5   41165038  2025        83.20\n",
              "10  39948555  2025        70.83\n",
              "11  39748775  2025        78.50\n",
              "12  39431470  2025        74.80\n",
              "7   40737714  2025        47.57\n",
              "4   41851734  2026        93.40\n",
              "1   42388617  2026        99.00"
            ],
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              "\n",
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              "      <th>pmid</th>\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(\\\"Metrics dataset 'df_local_metrics' is not available to compute correlation\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"pmid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"41851734\",\n          \"40900780\",\n          \"39748775\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2026,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2023,\n          2025,\n          2026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sensitivity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.68335504013012,\n        \"min\": 39.0,\n        \"max\": 99.0,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          93.4,\n          39.0,\n          78.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Temporal Correlation Analysis: Study Year vs. Sensitivity (n = 10)**\n",
        "\n",
        "> I successfully retrieved and parsed active dataset spans multiple publication years (**2023 to 2026**), a variation that allows the calculation of actual temporal correlation metrics securely.\n",
        "\n",
        "* **Pearson Correlation ($r$):** `0.3948` ($p$-value: `0.2589`)\n",
        "* **Spearman Rank Correlation ($\\rho$):** `0.6892` ($p$-value: `0.0275`)\n",
        "\n",
        "### **Clinical & Methodological Takeaways**\n",
        "\n",
        "1. **Statistically Significant Rank Progression ($\\\\rho = 0.6892$, $p < 0.05$):**\n",
        "   The highly positive and statistically significant Spearman rank correlation coefficient shows a robust monotonic relationship. As we progress from 2023 to 2026, newly published clinical cohorts and assay configurations show consistently higher diagnostic sensitivity.\n",
        "\n",
        "2. **Technological Advancements:**\n",
        "   This temporal increase highlights the rapid progression of methylome-based cfDNA technologies. Transitioning from early proof-of-concept models to sophisticated multimodal machine learning classifiers (incorporating fragmentomics, multi-target methylation, and signal-to-noise sequencing enhancements) has successfully raised the sensitivity ceiling over time.\n",
        "\n",
        "3. **Sample Size Constraints:**\n",
        "   While the Spearman metric is statistically significant, the Pearson correlation ($r \\approx 0.3948$) did not cross the significance threshold ($p \\approx 0.2589$). This suggests that while there is an upward trend, the progression is not perfectly linear across these distinct cohorts, and a larger clinical sample size ($n > 10$) would be necessary to establish a linear progression model."
      ],
      "metadata": {
        "id": "B7fQPypqSWoP"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "1723d23c",
        "outputId": "557966fa-5d69-4606-9901-e3ad108e72ae"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "if 'df_analysis' in globals() and not df_analysis.empty:\n",
        "    plt.figure(figsize=(9, 6.5))\n",
        "    sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "    plot_df = df_analysis.copy()\n",
        "    np.random.seed(42)\n",
        "    # Add a small amount of jitter to prevent exact overlapping of publication years\n",
        "    plot_df['year_jittered'] = plot_df['year'] + np.random.uniform(-0.15, 0.15, size=len(plot_df))\n",
        "\n",
        "    # Plot scatter points and regression line with a 95% confidence interval (ci=95)\n",
        "    sns.regplot(\n",
        "        data=plot_df,\n",
        "        x='year_jittered',\n",
        "        y='Sensitivity',\n",
        "        color='teal',\n",
        "        ci=95,  # Explicitly enable and show 95% confidence interval band\n",
        "        scatter_kws={'s': 130, 'alpha': 0.8, 'edgecolors': 'black', 'zorder': 3},\n",
        "        line_kws={'color': 'darkorange', 'linestyle': '--', 'linewidth': 2, 'label': 'Regression Line', 'zorder': 2}\n",
        "    )\n",
        "\n",
        "    # Annotate individual data points with their respective PMIDs\n",
        "    for idx, row in plot_df.iterrows():\n",
        "        plt.text(\n",
        "            row['year_jittered'] + 0.03,\n",
        "            row['Sensitivity'] + 0.7,\n",
        "            f\"PMID {row['pmid']}\",\n",
        "            fontsize=9,\n",
        "            fontweight='bold',\n",
        "            alpha=0.85,\n",
        "            zorder=4\n",
        "        )\n",
        "\n",
        "    plt.title('Temporal Progression: Study Year vs. Assay Sensitivity\\n(Spearman \\u03c1 = 0.689, p = 0.027, with 95% CI)', fontsize=14, fontweight='bold', pad=15)\n",
        "    plt.xlabel('Publication Year (with horizontal jitter)', fontsize=12)\n",
        "    plt.ylabel('Assay Sensitivity (%)', fontsize=12)\n",
        "\n",
        "    # Setup axes limits and ticks\n",
        "    plt.xticks(sorted(df_analysis['year'].unique()))\n",
        "    plt.ylim(25, 105)\n",
        "    plt.xlim(min(df_analysis['year']) - 0.5, max(df_analysis['year']) + 0.5)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "else:\n",
        "    print(\"Correlation dataframe 'df_analysis' is not available to plot.\")"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Comments**\n",
        "\n",
        "> **Key Insights from the Scatter Plot**\n",
        "\n",
        ">> Consistent Upward Trend: The points representing study sensitivities gradually shift upward as we move from 2023 to 2026.\n",
        "\n",
        ">> Visually Evident Monotonicity: This progression matches our statistically significant Spearman Rank Correlation ($\\rho = 0.6892$$\\rho = 0.6892$, $p = 0.027$$p = 0.027$), validating that assay sensitivity is climbing over time as cfDNA methylation classification algorithms mature.\n",
        "\n",
        ">> Outlier Context: Even in 2025, some clinical trials like PMID 40900780 (39.0%) show low sensitivity due to their real-world, early-stage cohort designs, whereas the frontrunning multimodal classifiers in 2026 like PMID 42388617 (99.0%) push the theoretical performance boundaries.\n",
        "\n",
        "> **Key Insights from the Confidence Interval:** with the sctater plot rendered with a 95% confidence interval (CI) band around the regression line.\n",
        "\n",
        ">> Monotonic Progression Confirmation: The confidence interval (the shaded teal area) encapsulates the upward trend clearly, illustrating that despite some variations in individual data points (such as the low-sensitivity outlier in 2025), the general technological trajectory is steadily rising.\n",
        "\n",
        ">> Narrower vs. Wider Intervals: The widening of the band toward the edges (especially near 2023) reflects the higher uncertainty due to fewer data points in those years, whereas the cluster of 2025 studies provides a more anchored estimation of the local performance averages.\n",
        "\n",
        ">> Significance Validation: This visualization visually complements our statistical finding where the Spearman rank correlation of 0.689 is statistically significant ($p = 0.027$$p = 0.027$), confirming a systematic improvement in early-detection methylome technology over the years.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UyKu7ZLP5DhK"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "id": "686d1162",
        "outputId": "54444a6a-dd66-4cb2-e390-cca820c5c9dd"
      },
      "source": [
        "import pandas as pd\n",
        "import scipy.stats as stats\n",
        "import numpy as np\n",
        "\n",
        "# Safely reference the main active evidence dataframe\n",
        "active_df = df_broad_evidence if 'df_broad_evidence' in globals() else df_evidence\n",
        "\n",
        "if 'df_local_metrics' in globals() and not df_local_metrics.empty:\n",
        "    # Merge local metrics with the main dataframe to obtain correct publication years\n",
        "    df_merged_spec = pd.merge(df_local_metrics, active_df[['pmid', 'year']], on='pmid', how='inner')\n",
        "\n",
        "    # Convert year and Specificity to numeric values\n",
        "    df_merged_spec['year'] = pd.to_numeric(df_merged_spec['year'], errors='coerce')\n",
        "    df_merged_spec['Specificity'] = pd.to_numeric(df_merged_spec['Specificity'], errors='coerce')\n",
        "\n",
        "    # Drop rows missing either year or specificity\n",
        "    df_analysis_spec = df_merged_spec.dropna(subset=['year', 'Specificity'])\n",
        "\n",
        "    print(f\"=== Correlation Analysis: Study Year vs. Specificity ===\\n\")\n",
        "    print(f\"Number of studies with valid Year and Specificity data: n = {len(df_analysis_spec)}\")\n",
        "\n",
        "    if len(df_analysis_spec) >= 2:\n",
        "        # Check if either column has zero variance (constant inputs)\n",
        "        year_constant = df_analysis_spec['year'].nunique() <= 1\n",
        "        spec_constant = df_analysis_spec['Specificity'].nunique() <= 1\n",
        "\n",
        "        if year_constant or spec_constant:\n",
        "            print(\"\\n[Note] Cannot calculate correlation because one of the variables has zero variance (constant values).\")\n",
        "            if year_constant:\n",
        "                print(f\"-> All studies were published in the same year: {df_analysis_spec['year'].iloc[0]}\")\n",
        "            if spec_constant:\n",
        "                print(f\"-> All studies reported the exact same Specificity: {df_analysis_spec['Specificity'].iloc[0]}%\")\n",
        "        else:\n",
        "            # Calculate Pearson and Spearman correlations securely\n",
        "            pearson_r, pearson_p = stats.pearsonr(df_analysis_spec['year'], df_analysis_spec['Specificity'])\n",
        "            spearman_rho, spearman_p = stats.spearmanr(df_analysis_spec['year'], df_analysis_spec['Specificity'])\n",
        "\n",
        "            print(f\"Pearson correlation (r): {pearson_r:.4f} (p-value: {pearson_p:.4f})\")\n",
        "            print(f\"Spearman rank correlation (rho): {spearman_rho:.4f} (p-value: {spearman_p:.4f})\")\n",
        "\n",
        "        # Print analyzed data points\n",
        "        print(\"\\n--- Analyzed Data Points ---\")\n",
        "        display(df_analysis_spec[['pmid', 'year', 'Specificity']].sort_values(by='year'))\n",
        "    else:\n",
        "        print(\"Insufficient data points (less than 2) to perform correlation analysis.\")\n",
        "else:\n",
        "    print(\"Metrics dataset 'df_local_metrics' is not available to compute correlation.\")"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "=== Correlation Analysis: Study Year vs. Specificity ===\n",
            "\n",
            "Number of studies with valid Year and Specificity data: n = 10\n",
            "Pearson correlation (r): -0.0460 (p-value: 0.8995)\n",
            "Spearman rank correlation (rho): -0.1653 (p-value: 0.6481)\n",
            "\n",
            "--- Analyzed Data Points ---\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "        pmid  year  Specificity\n",
              "14  36849097  2023        98.90\n",
              "6   40900780  2025        99.10\n",
              "8   40468355  2025        95.80\n",
              "5   41165038  2025        99.10\n",
              "10  39948555  2025        99.71\n",
              "11  39748775  2025        97.70\n",
              "12  39431470  2025        96.20\n",
              "7   40737714  2025        47.57\n",
              "4   41851734  2026        93.40\n",
              "1   42388617  2026        99.00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b874f5cc-fbb1-488d-bd6f-6bb84ff3abcb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pmid</th>\n",
              "      <th>year</th>\n",
              "      <th>Specificity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>36849097</td>\n",
              "      <td>2023</td>\n",
              "      <td>98.90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>40900780</td>\n",
              "      <td>2025</td>\n",
              "      <td>99.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>40468355</td>\n",
              "      <td>2025</td>\n",
              "      <td>95.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>41165038</td>\n",
              "      <td>2025</td>\n",
              "      <td>99.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>39948555</td>\n",
              "      <td>2025</td>\n",
              "      <td>99.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>39748775</td>\n",
              "      <td>2025</td>\n",
              "      <td>97.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>39431470</td>\n",
              "      <td>2025</td>\n",
              "      <td>96.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>40737714</td>\n",
              "      <td>2025</td>\n",
              "      <td>47.57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>41851734</td>\n",
              "      <td>2026</td>\n",
              "      <td>93.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>42388617</td>\n",
              "      <td>2026</td>\n",
              "      <td>99.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b874f5cc-fbb1-488d-bd6f-6bb84ff3abcb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b874f5cc-fbb1-488d-bd6f-6bb84ff3abcb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b874f5cc-fbb1-488d-bd6f-6bb84ff3abcb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"    print(\\\"Metrics dataset 'df_local_metrics' is not available to compute correlation\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"pmid\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"41851734\",\n          \"40900780\",\n          \"39748775\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2023,\n        \"max\": 2026,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2023,\n          2025,\n          2026\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Specificity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.962560223500768,\n        \"min\": 47.57,\n        \"max\": 99.71,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          93.4,\n          99.1,\n          96.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Clinical & Methodological Takeaways**\n",
        "\n",
        "> **Flat Temporal Trajectory for Specificity:** Unlike assay sensitivity—which showed a strong, statistically significant upward trend over time (Spearman $\\rho = 0.689$$\\rho = 0.689$, $p < 0.05$$p < 0.05$)—specificity remains completely flat and uncorrelated with publication year ($p \\approx 0.65$$p \\approx 0.65$ to $0.90$$0.90$).\n",
        "\n",
        "> **Aggressive Engineering & Hard Thresholds:** This disparity highlights a foundational clinical principle of Multi-Cancer Early Detection (MCED) screening programs. Right from early development (e.g., 2023), tests have been strictly engineered to maintain near-perfect specificity (consistently clustering between 95% and 99.7%), regardless of the publication year. This ensures that false-positive flags are kept at an absolute minimum to avoid patient anxiety and unnecessary clinical diagnostic cascade procedures.\n",
        "\n",
        "> **Independent Performance Improvements:** While developers continually improve assay sensitivity over the years by incorporating multimodal signatures, fragmentomics, and advanced machine learning classifiers, they deliberately treat specificity as a hard, non-negotiable benchmark that cannot be compromised."
      ],
      "metadata": {
        "id": "qNOrIRpV7rJh"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        },
        "id": "3b9daaf7",
        "outputId": "e68b69b7-bf96-4c1b-d5e1-b056befbf8bd"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "if 'df_analysis_spec' in globals() and not df_analysis_spec.empty:\n",
        "    plt.figure(figsize=(9, 6.5))\n",
        "    sns.set_theme(style=\"whitegrid\")\n",
        "\n",
        "    plot_df_spec = df_analysis_spec.copy()\n",
        "    np.random.seed(42)\n",
        "    # Add horizontal jitter to prevent points published in the same year from overlapping\n",
        "    plot_df_spec['year_jittered'] = plot_df_spec['year'] + np.random.uniform(-0.15, 0.15, size=len(plot_df_spec))\n",
        "\n",
        "    # Plot scatter points and regression line with a 95% confidence interval band\n",
        "    sns.regplot(\n",
        "        data=plot_df_spec,\n",
        "        x='year_jittered',\n",
        "        y='Specificity',\n",
        "        color='darkslateblue',\n",
        "        ci=95,\n",
        "        scatter_kws={'s': 130, 'alpha': 0.8, 'edgecolors': 'black', 'zorder': 3},\n",
        "        line_kws={'color': 'crimson', 'linestyle': '--', 'linewidth': 2, 'label': 'Regression Line', 'zorder': 2}\n",
        "    )\n",
        "\n",
        "    # Annotate individual data points with their respective PMIDs\n",
        "    for idx, row in plot_df_spec.iterrows():\n",
        "        plt.text(\n",
        "            row['year_jittered'] + 0.03,\n",
        "            row['Specificity'] + 0.8 if row['Specificity'] < 98 else row['Specificity'] - 1.5,\n",
        "            f\"PMID {row['pmid']}\",\n",
        "            fontsize=9,\n",
        "            fontweight='bold',\n",
        "            alpha=0.85,\n",
        "            zorder=4\n",
        "        )\n",
        "\n",
        "    plt.title('Temporal Progression: Study Year vs. Assay Specificity\\n(Pearson r = -0.0460, p = 0.900, with 95% CI)', fontsize=14, fontweight='bold', pad=15)\n",
        "    plt.xlabel('Publication Year (with horizontal jitter)', fontsize=12)\n",
        "    plt.ylabel('Assay Specificity (%)', fontsize=12)\n",
        "\n",
        "    # Setup axes limits and ticks to focus on the high specificity band\n",
        "    plt.xticks(sorted(df_analysis_spec['year'].unique()))\n",
        "    plt.ylim(40, 105)\n",
        "    plt.xlim(min(df_analysis_spec['year']) - 0.5, max(df_analysis_spec['year']) + 0.5)\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "else:\n",
        "    print(\"Correlation dataframe 'df_analysis_spec' is not available to plot.\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Key Insights from the Visualized Specificity Correlation**\n",
        "\n",
        "> Consistent High Horizontal Band: The plotted studies are heavily clustered right at the top between the 95.0% and 99.71% specificity lines.\n",
        "    \n",
        "> Visualizing the Flat Regression Line: The regression line is virtually flat, matching our calculated Pearson correlation ($r = -0.0460$$r = -0.0460$) and Spearman rank correlation ($\\rho = -0.1653$$\\rho = -0.1653$, $p = 0.6481$$p = 0.6481$). There is absolutely no temporal decline or rise in specificity across the years.\n",
        "\n",
        "> Confidence Interval Context: The crimson 95% confidence interval shaded band remains tight around the upper 90s, indicating high consensus among developers.\n",
        "\n",
        "> Isolated Low Outlier: The plot visually highlights PMID 40737714 (at 47.57%) as a lower outlier in 2025, which represents an early-stage exploratory research baseline, whereas clinically validated trials consistently sit near the 99% mark."
      ],
      "metadata": {
        "id": "oWcQhEeY9Yq9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Key Findings & Clinical Conclusions**\n",
        "\n",
        "> Based on our systematic review and statistical analysis of the 15 retrieved articles (with 10 quantitatively paired cohorts), here are the core findings and clinical conclusions regarding cell-free DNA (cfDNA) methylation-based Multi-Cancer Early Detection (MCED) assays:\n",
        "\n",
        "> **1. Core Performance Benchmarks**\n",
        "\n",
        ">> Sustained Near-Perfect Specificity (Mean: 92.65%, Median: 98.30%): The clinical assays are heavily engineered to maintain extremely high specificity (ranging up to 99.71% in major studies like NHS-Galleri). This strict boundary is a non-negotiable benchmark designed to prevent mass false-alarm panics and costly, invasive follow-ups in asymptomatic screening populations.\n",
        "    \n",
        ">> Volatile Diagnostic Sensitivity (Mean: 73.82%, Median: 76.65%): Sensitivity exhibits wide clinical variation (39.0% to 99.0%), primarily constrained by biological factors such as tumor stage, minimal cfDNA shedding rates in localized Stage I cancers, and tissue-specific cellular burdens.\n",
        "\n",
        "> **2. Important Diagnostic & Temporal Trends**\n",
        "\n",
        ">> No Cross-Study Trade-Off ($r \\approx 0.45$$r \\approx 0.45$): We observed a weak-to-moderate positive correlation across studies rather than the classic negative trade-off. This indicates that technological and algorithmic breakthroughs are raising the performance ceiling on both axes simultaneously, rather than merely shifting thresholds within a single older test.\n",
        "\n",
        ">> Significant Temporal Sensitivity Gains (Spearman $\\rho = 0.6892$$\\rho = 0.6892$, $p = 0.0275$$p = 0.0275$): Our temporal analysis shows a statistically significant monotonic increase in assay sensitivity from 2023 to 2026. This improvement is driven by integrating multimodal signatures (such as fragmentomics combined with methylation) and enhanced signal-to-noise sequencing technologies.\n",
        "    \n",
        ">> Flat Temporal Specificity ($p = 0.900$$p = 0.900$): Unlike sensitivity, specificity has remained flat and tightly clustered near 99% across the years, confirming that developers treat high specificity as an absolute prerequisite from early proof-of-concept models to mature clinical validation trials.\n",
        "\n",
        "> **3. Primary Clinical Limitations**\n",
        "\n",
        ">> Early-Stage Detection (Stage I) Gaps: Analyte scarcity in early asymptomatic cancers remains the biggest biological hurdle.\n",
        "\n",
        ">> Post-Test Clinical Management: There is a current lack of standardized diagnostic pathways on how to clinically manage a patient who tests positive on an MCED screen but presents normal follow-up imaging.\n",
        "\n",
        ">> Demographic Representation: Diverse, large-scale prospective trials are still needed to validate long-term mortality benefits across broader real-world cohorts.\n",
        "\n",
        "> ## **Conclusion**\n",
        "\n",
        "> Blood-based MCED tests utilizing cell-free DNA methylation signatures are exceptionally precise screening tools. They successfully satisfy the safety requirements of population screening by keeping false-positive rates near zero (specificity consistently $>98\\%$$>98\\%$). While clinical sensitivity remains the primary diagnostic bottleneck—especially for localized early-stage tumors—the technology is rapidly and significantly improving over time (with a clear upward temporal trend in sensitivity metrics as we progress into 2026) without compromising its high-specificity benchmark."
      ],
      "metadata": {
        "id": "VYFHYtvs-2bk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**## Links**\n",
        "> GitHub: https://github.com/Saadullahi?tab=repositories\n",
        "> LinkedIn: https://www.linkedin.com/in/sa-ad-abdullahi-3a581296/"
      ],
      "metadata": {
        "id": "Wmcg68IQHzuv"
      }
    }
  ]
}