Ma Peer Review

Ma Peer Review

Overview

Perform structured peer review and produce actionable feedback and validation checks.

Inputs

• 07_manuscript/ rendered outputs
• 06_analysis/validation.md
• 01_protocol/ and 03_screening/ artifacts

Outputs

• 08_reviews/reviewer1.md
• 08_reviews/reviewer2.md
• 08_reviews/action-items.md
• 08_reviews/grade_summary.csv
• 08_reviews/grade_summary.md
• 08_reviews/analysis_stats.json (collected from Stage 06)
• 08_reviews/grade_suggestions.csv (semi-automated)
• 08_reviews/grade_suggestions.md (semi-automated)
• 08_reviews/grade_detailed.md (per-domain reviewer decision format)
• 03_screening/round-01/quality_rob2.csv (RoB 2 for RCTs)
• 03_screening/round-01/rob2_assessment.md (RoB 2 narrative)
• 03_screening/round-01/quality_robins_i.csv (ROBINS-I for observational)
• 03_screening/round-01/robins_i_assessment.md (ROBINS-I narrative)

Workflow

1. Reviewer 1 focuses on methodology, inclusion criteria, and statistical validity.
   • Review 01_protocol/pico.yaml, 03_screening/round-01/agreement.md, 06_analysis/validation.md
   • Write to 08_reviews/reviewer1.md
2. Reviewer 2 focuses on clarity, reporting completeness, and reproducibility.
   • Review 07_manuscript/index.pdf, 07_manuscript/prisma_checklist.md
   • Write to 08_reviews/reviewer2.md
3. Record issues with severity, location, and recommended fixes.
   • Write to 08_reviews/action-items.md (columns: issue_id, severity, location, recommendation, status)
4. Create a consolidated action list with owners and status.
   • Update 08_reviews/action-items.md
5. Initialize GRADE summary tables with scripts/init_grade_summary.py via uv run.
   • Use scripts/init_grade_summary.py
   • Read from 05_extraction/extraction.csv (outcomes)
   • Write to 08_reviews/grade_summary.csv (columns: outcome, n_studies, n_participants, effect_estimate, certainty, reasons)
   • Write to 08_reviews/grade_summary.md
6. Collect analysis statistics from Stage 06 outputs with scripts/collect_analysis_stats.py.
   • Parses markdown reports and CSV tables for I², Egger's test, CI bounds, total events
   • Optionally includes RoB 2 assessment summary via --rob-csv
   • Write to 08_reviews/analysis_stats.json
7. Generate semi-automated GRADE suggestions with scripts/auto_grade_suggestion.py.
   • Use --stats 08_reviews/analysis_stats.json to enable computed suggestions
   • Use --out-detailed-md for per-domain reviewer decision format
   • Write to 08_reviews/grade_suggestions.csv, grade_suggestions.md, grade_detailed.md

Resources

• references/reporting-checks.md for PRISMA-style reporting checks.
• references/grade-template.md for GRADE evidence profiling.
• references/grade-summary-schema.md for GRADE summary columns.
• references/rob2-template.md for RoB 2 per-study assessment (RCTs).
• references/robins-i-template.md for ROBINS-I per-study assessment (observational).
• scripts/init_grade_summary.py for generating GRADE summary tables.
• scripts/collect_analysis_stats.py for extracting statistics from Stage 06 outputs.
• scripts/auto_grade_suggestion.py for semi-automated certainty suggestions with computed rationale.
• scripts/init_rob2_assessment.py for initializing RoB 2 assessment tables.
• scripts/init_robins_i_assessment.py for initializing ROBINS-I assessment tables.

Validation

• Verify that methods and results are consistent with the protocol.
• Confirm that all outputs are reproducible from the stored data and scripts.

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