peer-review

name	peer-review
description	Conduct professional peer reviews for papers or theses, providing structured evaluations and improvement suggestions; use when you need a pre-submission assessment, an internal review, or academic quality control.
license	MIT
author	AIPOCH

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

Peer Review

When to Use

• Pre-submission manuscript check: Before submitting to a journal/conference to identify major risks and revision priorities.
• Internal lab/group review: For advisor or team quality control prior to external dissemination.
• Thesis/dissertation evaluation: To assess academic rigor, structure, and defensibility before committee review.
• Revision planning after feedback: To translate reviewer/editor comments into an actionable improvement roadmap.
• Quality assurance for research outputs: To ensure methods, reporting, and conclusions meet disciplinary standards.

Key Features

• Structured end-to-end review workflow: Overall evaluation → methods/results check → issue organization → recommendation.
• Major vs. minor issue triage: Separates publication-blocking problems from polish-level improvements.
• Actionable revision suggestions: Each issue is paired with concrete steps to fix or strengthen the work.
• Recommendation with rationale: Clear accept/revise/reject guidance with reasons and improvement path.
• Reusable templates and checklists: Supports consistent formatting and comprehensive coverage (see referenced files).

Dependencies

• None (runtime)

Example Usage

Use the template to produce a structured review.

1. Open the template:

   • assets/peer_review_template.md

2. Fill it using the workflow below. Example (copy/paste and complete):

# Peer Review Report

## 1. Overall Evaluation
**Summary of the work:**  
This paper investigates [research question] by using [method/data]. The main contributions are: (1) [...], (2) [...].

**Novelty and significance:**  
- Novelty: [high/medium/low] because [...]
- Significance: [high/medium/low] because [...]

## 2. Methods and Results
**Research design and methodology:**  
- Appropriateness of design: [...]
- Data and sampling: [...]
- Statistical/analytical methods: [...]
- Reproducibility (code/data availability, parameter reporting): [...]

**Results vs. conclusions:**  
- Do results support claims? [...]
- Alternative explanations addressed? [...]
- Robustness checks/ablation/sensitivity analysis: [...]

## 3. Issues and Revision Suggestions

### Major Issues (must address)
1. **Issue:** [...]
   - **Why it matters:** [...]
   - **Suggested fix:** [...]
   - **Expected impact:** [...]

2. **Issue:** [...]
   - **Why it matters:** [...]
   - **Suggested fix:** [...]
   - **Expected impact:** [...]

### Minor Issues (should address)
1. **Issue:** [...]
   - **Suggested fix:** [...]

2. **Issue:** [...]
   - **Suggested fix:** [...]


 Accept / Minor Revision / Major Revision / Reject

  
Explain the decision based on novelty, rigor, clarity, and evidence strength.

  
List the top 3–5 changes that would most improve the manuscript.

For output formats, checklists, and inspection points, see:

• references/guide.md

Implementation Details

Review Workflow (Algorithm)

1. Read for global understanding

   • Read the abstract and full text to form an overall impression.
   • Identify the research question, claimed contributions, and target audience/venue.

2. Overall evaluation

   • Summarize the research questions and major contributions.
   • Assess novelty (what is new vs. prior work) and significance (why it matters).

3. Methods and results verification

   • Check research design, data quality, and statistical/analytical methods for correctness and suitability.
   • Evaluate whether results logically and quantitatively support the conclusions.
   • Flag missing details that prevent replication (e.g., parameters, datasets, baselines, evaluation protocol).

4. Issue organization

   • Classify findings into:
     • Major issues: validity threats, methodological flaws, unsupported claims, missing critical experiments, ethical/compliance gaps.
     • Minor issues: clarity, formatting, citations, small inconsistencies, language improvements.
   • For each issue, provide an actionable revision suggestion (what to change and how).

5. Recommendation

   • Provide a decision (accept/revise/reject) aligned with the severity and fixability of major issues.
   • Explain the rationale and provide a prioritized improvement path.

Key Parameters / Criteria

• Novelty: degree of differentiation from prior work; clarity of contribution statement.
• Significance: practical/theoretical impact; relevance to the field and venue.
• Rigor: appropriateness of methods; correctness of analysis; robustness checks.
• Evidence alignment: strength of support from results to claims; avoidance of overgeneralization.
• Reproducibility: completeness of experimental details; availability of data/code; transparent reporting.
• Clarity and structure: logical flow, readability, figure/table quality, and citation completeness.

Templates and References

• Template (preferred for structured output): assets/peer_review_template.md
• Guidance/checklists/output formats: references/guide.md