Cohort Study Quality Assessment Nos

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:

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/calculate_nos_score.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/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

1. Input: The full text of the cohort study article (copy-pasted or provided by user).
2. Process:
   • Extract study metadata (Author, Year).
   • Evaluate the study against NOS criteria (Selection, Comparability, Outcome).
   • Calculate the total score (number of stars).
3. Output: A Markdown table summarizing the assessment for each criterion and the overall score.

Method 2: PDF File Input

1. Input: Path to a PDF file containing the cohort study.
2. Process:
   • Use scripts/extract_pdf.py to extract text from the PDF.
   • Review the extracted text for completeness.
   • Proceed with Method 1 steps.
3. 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.