study-design-scale-selector

name	study-design-scale-selector
description	Determines the appropriate Risk of Bias assessment scale for a medical study based on its design (RCT, Cohort, etc.), using PubMed metadata lookup or text analysis. Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract).
license	MIT
author	AIPOCH

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

Study Design Scale Selector

This skill helps identify the study design of a medical paper and selects the appropriate risk of bias assessment scale.

When to Use

• Use this skill when the request matches its documented task boundary.
• Use it when the user can provide the required inputs and expects a structured deliverable.
• Prefer this skill for repeatable, checklist-driven execution rather than open-ended brainstorming.

Key Features

• Scope-focused workflow aligned to: Determines the appropriate Risk of Bias assessment scale for a medical study based on its design (RCT, Cohort, etc.), using PubMed metadata lookup or text analysis. Use when the user wants to know which quality assessment tool to use for a specific paper (given PMID or abstract).
• 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

cd "20260316/scientific-skills/Data Analytics/study-design-scale-selector"
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 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.

Workflow

1. Check Metadata (If PMID provided)

If the user provides a PMID, use the selector.py script to fetch study metadata from PubMed.

python scripts/selector.py "<PMID>"

If the script returns a non-empty JSON with study_design:

• Use the returned study_design.
• Skip to Step 3.

If the script returns empty JSON {} or fails:

• Proceed to Step 2.

2. Analyze Text (Fallback)

If metadata is unavailable or no PMID is provided, analyze the Title and Abstract provided by the user.

Action: Identify the study design from the text. Look for keywords like:

• "Randomized controlled trial", "RCT"
• "Cohort study", "Longitudinal study"
• "Case-control study"
• "Cross-sectional study"

3. Select Scale

Using the identified study_design, consult scale_rules.md to select the correct assessment scale.

4. Output

Present the result in the following JSON format:

{
  "study_design": "<Identified Design>",
  "scale": "<Selected Scale>"
}

Helper Scripts

PDF Text Extraction

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

When Not to Use

• Do not use this skill when the required source data, identifiers, files, or credentials are missing.
• Do not use this skill when the user asks for fabricated results, unsupported claims, or out-of-scope conclusions.
• Do not use this skill when a simpler direct answer is more appropriate than the documented workflow.

Required Inputs

• A clearly specified task goal aligned with the documented scope.
• All required files, identifiers, parameters, or environment variables before execution.
• Any domain constraints, formatting requirements, and expected output destination if applicable.

Output Contract

• Return a structured deliverable that is directly usable without reformatting.
• If a file is produced, prefer a deterministic output name such as study_design_scale_selector_result.md unless the skill documentation defines a better convention.
• Include a short validation summary describing what was checked, what assumptions were made, and any remaining limitations.

Validation and Safety Rules

• Validate required inputs before execution and stop early when mandatory fields or files are missing.
• Do not fabricate measurements, references, findings, or conclusions that are not supported by the provided source material.
• Emit a clear warning when credentials, privacy constraints, safety boundaries, or unsupported requests affect the result.
• Keep the output safe, reproducible, and within the documented scope at all times.

Failure Handling

• If validation fails, explain the exact missing field, file, or parameter and show the minimum fix required.
• If an external dependency or script fails, surface the command path, likely cause, and the next recovery step.
• If partial output is returned, label it clearly and identify which checks could not be completed.

Quick Validation

Run this minimal verification path before full execution when possible:

python scripts/extract_pdf.py --help

Expected output format:

Result file: study_design_scale_selector_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any