Source: https://github.com/aipoch/medical-research-skills
Cohort Study Quality Assessment (NOS)
This skill evaluates the quality of a cohort study based on the Newcastle-Ottawa Scale (NOS). It analyzes Selection, Comparability, and Outcome categories and generates a scored report.
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: Evaluates the quality of cohort studies using the Newcastle-Ottawa Scale (NOS). Use when the user provides a cohort study article or text and needs a quality assessment report.
- Packaged executable path(s):
scripts/calculate_nos_score.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
See ## Prerequisites above for related details.
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/cohort-study-quality-assessment-nos"
python -m py_compile scripts/calculate_nos_score.py
python scripts/calculate_nos_score.py --help
Example run plan:
- Confirm the user input, output path, and any required config values.
- Edit the in-file
CONFIG block or documented parameters if the script uses fixed settings.
- Run
python scripts/calculate_nos_score.py with the validated inputs.
- 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/calculate_nos_score.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.
Prerequisites
- Python 3.x
- PyPDF2 library (
pip install PyPDF2)
- Access to
references/nos_criteria.md for detailed evaluation criteria
Usage
Method 1: Direct Text Input
- Input: The full text of the cohort study article (copy-pasted or provided by user).
- Process:
- Extract study metadata (Author, Year).
- Evaluate the study against NOS criteria (Selection, Comparability, Outcome).
- Calculate the total score (number of stars).
- Output: A Markdown table summarizing the assessment for each criterion and the overall score.
Method 2: PDF File Input
- Input: Path to a PDF file containing the cohort study.
- Process:
- Use
scripts/extract_pdf.py to extract text from the PDF.
- Review the extracted text for completeness.
- Proceed with Method 1 steps.
- Output: Same as Method 1.
Workflow
Step 1: Extract Content
If input is PDF:
cd "D:\helix\test record\\262\skills_\skills\cohort-study-quality-assessment-nos"
python scripts/extract_pdf.py "path/to/your/file.pdf"
Note: The extracted text will be saved to extracted_text.txt in the current directory.
Step 2: Analyze the Text
You must analyze the input text to extract information and evaluate it against the criteria defined in references/nos_criteria.md.
1. Extract Metadata:
- Study: First Author, Year (e.g., "Wang, 2018").
2. Evaluate Selection (4 items):
- D1: Representativeness of the Exposed Cohort (* or -)
- Look for: study design (retrospective/prospective), data source (hospital registry/population-based), inclusion/exclusion criteria clarity
- Give star if: representative sample from a defined population with clear criteria
- D2: Selection of the Non-Exposed Cohort (* or -)
- Look for: comparison group source (same community/hospital/different source)
- Give star if: drawn from the same source as exposed cohort
- D3: Ascertainment of Exposure (* or -)
- Look for: how exposure was determined (medical records/interview/self-report)
- Give star if: secure record (surgical records, pharmacy records, EMR) or structured interview
- D4: Outcome not present at start (* or -)
- Look for: exclusion of patients with outcome at baseline
- Give star if: demonstrated that outcome was not present at study start
3. Evaluate Comparability (2 items):
- D5: Age comparability (* or -)
- Look for: age matching or statistical adjustment for age
- Give star if: matched in design OR adjusted in analysis (not just "no difference" statement)
- D6: Additional comparability (* or -)
- Look for: matching/adjustment for other key confounders (BMI, disease severity, comorbidities)
- Give star if: matched in design OR adjusted in analysis for important factors beyond age
4. Evaluate Outcome (3 items):
- D7: Assessment of Outcome (* or -)
- Look for: outcome measurement method (blind assessment/secure records/self-report)
- Give star if: independent blind assessment, secure records (lab results, imaging), or record linkage
- D8: Enough follow-up (* or -)
- Look for: duration of follow-up
- Give star if: follow-up long enough for outcomes to occur (depends on disease/condition)
- D9: Adequacy of follow up (* or -)
- Look for: follow-up completion rate, description of losses
- Give star if: complete follow-up OR ≥80% follow-up with description of losses
- Important: If follow-up rate not reported or <80%, do NOT give star
For each item, determine if it meets the criteria for a star (*). If not, or if uncertain, mark as (-).
Step 3: Format Data
Construct a JSON object with the results:
{
"Study": "Wang, 2018",
"D1": "*",
"D2": "-",
"D3": "*",
"D4": "*",
"D5": "*",
"D6": "-",
"D7": "*",
"D8": "*",
"D9": "-"
}
Scoring Notes:
- Be conservative: If uncertain about meeting criteria, use "-"
- For D9 (follow-up adequacy): Common issue is lack of reported completion rate
- Document your reasoning for each item
Step 4: Calculate Score and Generate Report
Run the python script to generate the final table:
python scripts/calculate_nos_score.py '<json_string>'
Example:
python scripts/calculate_nos_score.py "{\"Study\": \"Wei et al., 2026\", \"D1\": \"*\", \"D2\": \"*\", \"D3\": \"*\", \"D4\": \"*\", \"D5\": \"*\", \"D6\": \"*\", \"D7\": \"*\", \"D8\": \"*\", \"D9\": \"-\"}"
Important: JSON string must be properly escaped when passed via command line.
Step 5: Generate Final Report
Return to user:
- The generated Markdown table
- Brief explanation of each scoring decision
- Summary of study quality (High: ≥7 stars, Moderate: 4-6 stars, Low: <4 stars)
- Key strengths and limitations
Helper Scripts
PDF Text Extraction
When the user provides a PDF file path, use scripts/extract_pdf.py to extract the text content before assessment:
Features:
- Extracts text from all pages
- Saves output to
extracted_text.txt
- Handles path issues with spaces
- Provides progress feedback
Usage:
python scripts/extract_pdf.py "path/to/file.pdf"
Output:
- Console: Extraction progress and statistics
- File:
extracted_text.txt in current working directory
Quality Interpretation
| Score | Quality Level | Recommendation |
|---|
| 9 stars | Excellent | Low risk of bias, high confidence |
| 7-8 stars | High quality | Acceptable for meta-analysis |
| 4-6 stars | Moderate quality | Consider in sensitivity analyses |
| <4 stars | Low quality | High risk of bias, use caution |
Common Issues and Solutions
- PDF extraction fails: Check if file exists and is not corrupted; try different PDF library (PyMuPDF)
- JSON parsing error: Ensure proper escaping of quotes in command line
- Uncertain criteria: When in doubt, be conservative and assign "-"
- Missing information: Note in report that certain items could not be assessed
Input Validation
This skill accepts requests that match the documented purpose of cohort-study-quality-assessment-nos and include enough context to complete the workflow safely.
Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:
cohort-study-quality-assessment-nos only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.
References
- Detailed criteria:
references/nos_criteria.md
- Wells GA, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses
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
cohort_study_quality_assessment_nos_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/calculate_nos_score.py --help
Expected output format:
Result file: cohort_study_quality_assessment_nos_result.md
Validation summary: PASS/FAIL with brief notes
Assumptions: explicit list if any