Ma Peer Review

Overview

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

Inputs

• 07_manuscript/ rendered outputs
• 06_analysis/validation.md
• 01protocol/ and 03screening/ artifacts

Outputs

• 08_reviews/reviewer1.md
• 08_reviews/reviewer2.md
• 08_reviews/action-items.md
• 08reviews/gradesummary.csv
• 08reviews/gradesummary.md
• 08reviews/analysisstats.json (collected from Stage 06)
• 08reviews/gradesuggestions.csv (semi-automated)
• 08reviews/gradesuggestions.md (semi-automated)
• 08reviews/gradedetailed.md (per-domain reviewer decision format)
• 03screening/round-01/qualityrob2.csv (RoB 2 for RCTs)
• 03screening/round-01/rob2assessment.md (RoB 2 narrative)
• 03screening/round-01/qualityrobins_i.csv (ROBINS-I for observational)
• 03screening/round-01/robinsi_assessment.md (ROBINS-I narrative)

Workflow

1. Reviewer 1 focuses on methodology, inclusion criteria, and statistical validity.

- Review 01protocol/pico.yaml, 03screening/round-01/agreement.md, 06analysis/validation.md - Write to 08reviews/reviewer1.md

1. Reviewer 2 focuses on clarity, reporting completeness, and reproducibility.

- Review 07manuscript/index.pdf, 07manuscript/prismachecklist.md - Write to 08reviews/reviewer2.md

1. Record issues with severity, location, and recommended fixes.

- Write to 08reviews/action-items.md (columns: issueid, severity, location, recommendation, status)

1. Create a consolidated action list with owners and status.

- Update 08_reviews/action-items.md

1. Initialize GRADE summary tables with scripts/initgradesummary.py via uv run.

- Use scripts/initgradesummary.py - Read from 05extraction/extraction.csv (outcomes) - Write to 08reviews/gradesummary.csv (columns: outcome, nstudies, nparticipants, effectestimate, certainty, reasons) - Write to 08reviews/gradesummary.md

1. Collect analysis statistics from Stage 06 outputs with scripts/collectanalysisstats.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 08reviews/analysisstats.json

1. Generate semi-automated GRADE suggestions with scripts/autogradesuggestion.py.

- Use --stats 08reviews/analysisstats.json to enable computed suggestions - Use --out-detailed-md for per-domain reviewer decision format - Write to 08reviews/gradesuggestions.csv, gradesuggestions.md, gradedetailed.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/initgradesummary.py for generating GRADE summary tables.
• scripts/collectanalysisstats.py for extracting statistics from Stage 06 outputs.
• scripts/autogradesuggestion.py for semi-automated certainty suggestions with computed rationale.
• scripts/initrob2assessment.py for initializing RoB 2 assessment tables.
• scripts/initrobinsi_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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