Clinical Study Info Extractor

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

See ## Usage above for related details.

cd "20260316/scientific-skills/Evidence Insight/clinical-study-info-extractor"
python -m py_compile scripts/utils.py
python scripts/utils.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/utils.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/utils.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. Input Normalization: Splits and cleans the input string of PMIDs.
2. Literature Search: Queries the PubMed API directly to fetch document details.
3. Information Extraction: Iterates through documents to extract fields (Title, Year, Journal, Abstract, DOI, Type, Population, Sample Size, Intervention, Results, Conclusion).
4. Verification: Enforces quality rules (e.g., sample size only for research articles).
5. Output Formatting: Aggregates results into a Chinese Markdown table.

Usage

When you have a list of PMIDs and need structured details:

1. Normalize Input: Use scripts/utils.py with normalize_pmids to parse the input string.

2. Search & Process: Use scripts/utils.py with fetch_pubmed_data to query PubMed and get a list of document JSON strings.

3. Extract & Verify: For each document, use the prompts defined in references/extraction_rules.md to extract and verify information.

   • Step 1: Extraction
   • Step 2: Verification

4. Format Output: Use scripts/utils.py with format_table to generate the final Markdown table.

Quality Rules

See references/extraction_rules.md for detailed extraction logic and constraints.

• Article Type: Must be one of Research, Meta-analysis, Case Report, Review.
• Sample Size: Numeric only, empty for non-research.
• Intervention: Single column, "None" if not mentioned.
• Language: All Chinese except Journal Name.