Source: https://github.com/aipoch/medical-research-skills

Paper Screening (Full Text + PubMed)

This skill screens a medical paper to determine if it should be included in a meta-analysis based on PICO criteria. It can optionally fetch metadata (Title/Abstract) from PubMed if a PMID is provided.

When to Use

• Use this skill when you need screen full-text papers against inclusion/exclusion criteria, with optional pubmed metadata check using pmid. use when the user needs to evaluate a paper for a meta-analysis in a reproducible workflow.
• Use this skill when a data analytics task needs a packaged method instead of ad-hoc freeform output.
• Use this skill when the user expects a concrete deliverable, validation step, or file-based result.
• Use this skill when

  scripts/extract_pdf.py

  is the most direct path to complete the request.
• Use this skill when you need the

  meta-screening-fulltext

  package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Screen full-text papers against inclusion/exclusion criteria, with optional PubMed metadata check using PMID. Use when the user needs to evaluate a paper for a meta-analysis.
• Packaged executable path(s):

  scripts/extract_pdf.py

  .
• Reference material available in

  references/

  for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python

  :

  3.10+

  . Repository baseline for current packaged skills.

• Third-party packages

  :

  not explicitly version-pinned in this skill package

  . Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260316/scientific-skills/Data Analytics/meta-screening-fulltext"
python -m py_compile scripts/extract_pdf.py
python scripts/extract_pdf.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file

   CONFIG

   block or documented parameters if the script uses fixed settings.
3. Run

   python scripts/extract_pdf.py

   with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See

## Workflow

above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface:

  scripts/extract_pdf.py

  .
• Reference guidance:

  references/

  contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Workflow

1. Analyze Inputs:

   • input_paper

     : Full text of the paper.

   • inclu_exclu_criterion

     : Inclusion/Exclusion criteria.

   • input_pmid

     (Optional): PMID of the paper.

2. Check PubMed (Optional):

   • If

     input_pmid

     is provided, run

     scripts/query_pubmed.py

     to fetch Title and Abstract.
   • Command:

     python scripts/query_pubmed.py "<input_pmid>"

3. Screen Paper:

   • Scenario A: PubMed Hit: If the script returns metadata, compare the criteria against this data (Title + Abstract).
   • Scenario B: No PubMed Data: Compare the criteria against

     input_paper

     (full text).
   • Use the appropriate prompt from

     references/screening_prompts.md

     .

4. Format Output:

   • Ensure the output is a JSON object with

     Result

     ("Include" or "Exclude") and

     Reason

     .
   • If "Exclude", the reason must be one of the standard exclusion categories (Wrong population, etc.).

Quality Rules

• Evidence-Based: Decisions must be based strictly on the provided text or retrieved metadata.
• Structured Output: Final output must always be parseable JSON.
• Exclusion Reasons: Must use standard terminology: "Wrong population", "Wrong intervention", "Wrong comparator", "Wrong outcomes", "Wrong study design".

Helper Scripts

PDF Text Extraction

When the user provides a PDF file path, use

extract_pdf.py

to extract the text content before assessment: