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: