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

QUAPAS Bias Evaluator

When to Use

• Use this skill when you need evaluates bias in medical literature (prognosis studies) using quapas criteria. use when the user wants to assess the quality or risk of bias of a medical paper text 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 quapas-quality-assessment for prognosis studies package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Evaluates bias in medical literature (prognosis studies) using QUAPAS criteria. Use when the user wants to assess the quality or risk of bias of a medical paper text.
• 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/quapas-quality-assessment-for-prognosis-studies"
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.

Description

This skill evaluates the risk of bias in prognosis studies using the Quality of Prognosis Studies (QUAPAS) tool. It analyzes 5 domains: Participants, Index Test, Outcome, Flow and Timing, and Analysis.

Workflow

1. Input: The user provides the full text of a medical paper.

2. Study Extraction:

   • Extract the first author's name and year (e.g., "Wang, 2018").

3. Domain Analysis: For each of the 5 domains, analyze the text using the questions defined in references/quapas_prompts.md.

   • Domain 1: Participants
   • Domain 2: Index Test
   • Domain 3: Outcome
   • Domain 4: Flow and Timing
   • Domain 5: Analysis

4. Risk of Bias (ROB) Assessment: For each domain, determine the Risk of Bias (Low, High, Unclear) based on the answers to the signaling questions:

   • If all answers are "Yes" -> Low Risk.
   • If any answer is "No" -> High Risk.
   • If information is missing -> Unclear.

5. Overall Judgment: Determine the overall risk of bias for the study based on the domain results.

   • If most domains are Low Risk -> Low Overall Bias.
   • If key domains are High Risk -> High Overall Bias.

6. Final Output: Generate a JSON object strictly following the schema below:

   {
     "study": "Author, Year",
     "D1": "Low|High|Unclear",
     "D2": "Low|High|Unclear",
     "D3": "Low|High|Unclear",
     "D4": "Low|High|Unclear",
     "D5": "Low|High|Unclear",
     "overall": "Low|High|Unclear"
   }

References

• See references/quapas_prompts.md for detailed signaling questions and prompt logic.

Helper Scripts

PDF Text Extraction

When the user provides a PDF file path, use extract_pdf.py to extract the text content before assessment: