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

QUADAS-C Assessment Skill

This skill automates the risk of bias assessment for diagnostic accuracy studies comparing two or more index tests (QUADAS-C).

When to Use

• Use this skill when you need automated bias assessment for diagnostic accuracy studies using quadas-c criteria. requires full text input 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 quadas-c-assessment for diagnostic accuracy studies package behavior rather than a generic answer.

Key Features

• Scope-focused workflow aligned to: Automated bias assessment for diagnostic accuracy studies using QUADAS-C criteria. Requires full text input.
• Packaged executable path(s): scripts/extract_pdf.py plus 1 additional script(s).
• 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

See ## Usage above for related details.

cd "20260316/scientific-skills/Data Analytics/quadas-c-assessment-for-diagnostic-accuracy-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

• 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 with additional helper scripts under scripts/.
• 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.

When to Use This Skill

Use this skill when:

1. You have the full text of a clinical research paper.
2. You need to assess the risk of bias using the QUADAS-C tool.
3. The study compares at least two diagnostic methods.

Usage

The skill processes the paper through the following steps:

1. Extraction: Identifies diagnostic methods compared in the study.
2. Assessment: For each method, runs a QUADAS-2 assessment.
3. Signaling Questions: Answers specific QUADAS-C signaling questions for 4 domains:
   • Patient Selection
   • Index Test
   • Reference Standard
   • Flow and Timing
4. Risk of Bias: Determines "Low", "High", or "Unclear" risk for each domain.
5. Reporting: Generates a structured JSON report.

Execution

To run the assessment, use the provided Python script. You can pass the paper text as a command-line argument or via a file.

# Example: Process a text file containing the paper
python scripts/quadas_c.py --file "path/to/paper.txt"

Output Format

The output is a JSON object with the following structure:

{
  "P": "Low/High/Unclear",
  "I": "Low/High/Unclear",
  "R": "Low/High/Unclear",
  "FT": "Low/High/Unclear"
}

Reference

See references/prompts.md for the specific signaling questions and risk of bias criteria used in the LLM prompts.

Helper Scripts

PDF Text Extraction

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