Quality Assessment

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

Quality Assessment

This skill is developed based on the scales recommended by the Latitudes Network. The workflow is as follows: Identify Study Design: First, determine the study design of the literature (Query: Study design) Recommend Quality Assessment Tool: Based on the identified study design, query and select the appropriate quality assessment tool (Query: References - Quality Assessment Tool) Complete Quality Assessment: Use the selected tool to perform the final quality assessment of the literature

Study design

• Animal studies
• Case series
• Case-control studies
• Cohort studies
• Cross-sectional studies
• Diagnostic test accuracy (DTA) studies
• Economic evaluations
• General reviews
• Guidelines
• Interrupted time series
• Laboratory studies
• Mixed methods
• Natural experiments
• Network meta-analyses
• Non-randomized studies of interventions
• Observational studies (mixed designs)
• Prediction models
• Prevalence studies
• Prognostic accuracy studies
• Qualitative studies
• Quasi-experimental
• Randomized controlled trials (RCT)
• Reliability studies
• Systematic reviews
• Umbrella systematic reviews
• Uncontrolled single-arm studies

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: Automates critical appraisal and quality assessment for research papers by analyzing text against established methodological standards (such as risk of bias tools, quality checklists, or reporting guidelines) and synthesizing a structured evaluation report. Use when you need to assess the methodological quality, internal validity, or reporting completeness of any type of study—including RCTs, observational studies, systematic reviews, qualitative research, or diagnostic accuracy studies.
• 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/quality-assessment"
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.
• 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.

Tools （See references-quality assessment tool）

Helper Scripts

PDF Text Extraction

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

python extract_pdf.py

This script will:

• Extract text from the PDF file (e.g., gefitinib nejmoa0810699.pdf)
• Save the extracted text to full_text.txt
• Handle multi-page documents with proper page separators

Usage flow:

1. User provides PDF file path
2. Run python extract_pdf.py to extract text
3. Read the generated full_text.txt file
4. Perform quality assessment on the extracted text

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.