radiology-prereview

name

radiology-prereview

description

Run a rigorous pre-submission mock peer review of an imaging-AI / radiomics / radiogenomics manuscript — simulate the methods, statistics, reporting-guideline, figure, citation/claim-verification, and data-sharing reviewer a top journal would assign, and surface the issues that cause desk-reject or major revision before submission. Use when the user wants a mock review, pre-submission audit, "投稿前预审/模拟审稿", "find the holes before a reviewer does", a two-pass abstract/figure/table claim audit, or a readiness check. Returns a reviewer-style report with Blocker / Major / Minor issues, each tied to the manuscript location and the reporting-guideline or methodological risk, plus an editor-style recommendation and a prioritised fix order. Never fabricates compliance or papers over a real weakness.

Pre-submission Mock Review

Use this skill to be the harshest fair reviewer before the real one is. It reads the manuscript the way a methods-literate Radiology/Lancet-DH/Nature-Medicine reviewer would, finds the dealbreakers, and returns a reviewer-style report you can act on — so issues are fixed on your terms, not surfaced in a rejection.

Core stance

• Adversarial but on the author's side. Hunt for the weakness a reviewer will weaponise, then hand back the fix — not just the criticism.
• Dealbreakers first. No patient-level split, data leakage, no external validation, undefined labels, unclear segmentation, incomplete statistics, overclaiming — these decide the outcome. Triage them before cosmetics.
• Map to the guideline. Tie each issue to the specific CLAIM/CLEAR/TRIPOD+AI/STARD/IBSI item or methodological risk a reviewer would cite (→ radiology-reporting).
• Check the claims against the evidence. Does the abstract/Discussion overstate AUC, correlation, or retrospective results? Flag every claim the data don't support.
• Honest readiness verdict. Give an editor-style recommendation (ready / minor / major / not yet) with the reasons — don't reassure.
• Integrity. Never invent compliance, never wave through a real weakness to be encouraging.

When to use

• "Mock-review my paper before I submit." / "投稿前帮我模拟审稿、做预审。"
• "Find the holes a reviewer will find."
• "Is this ready for [target journal], or what must I fix first?"
• After drafting, before radiology-journal selection and submission.

When to open extra files

File	Open when
references/review-dimensions.md	The full set of dimensions to review (design, data, labels, leakage, stats, reporting, figures, claims, sharing)
references/dealbreakers.md	The hard issues that trigger desk-reject / major revision, with how to detect and fix each
references/review-report-format.md	The reviewer-report + editor-recommendation output structure
references/pre-submission-hard-gates.md	Final submission readiness audit, rejected-paper rescue, contribution map, reviewer objection register, or when deciding whether a paper is truly ready
references/ai-radiogenomics-pitfall-audit.md	Imaging-AI, foundation-model, VLM, radiomics, deep radiomics, or radiogenomics manuscripts need a targeted audit for leakage, external validation, site/scanner confounding, superficial XAI, weak clinical utility, or mechanism overclaim
references/claim-verification-gate.md	Submission-facing abstract, Key Results, figure legend, table, graphical abstract, novelty, comparison, and numerical claims need two-pass extraction and verification

Workflow

1. Intake — manuscript (or sections), study type, target journal/tier if known.
2. Classify the study and load the dimensions (review-dimensions.md); pull the right guideline stack via radiology-reporting.
3. For final readiness checks, open pre-submission-hard-gates.md and score each hard gate as PASS / CONDITIONAL / FAIL before writing softer reviewer comments.
4. Hunt dealbreakers (dealbreakers.md) — partition hygiene, leakage, external validation, labels/reference standard, segmentation reproducibility, statistical completeness, overclaim, data/code availability.
5. For AI/radiogenomics manuscripts, open ai-radiogenomics-pitfall-audit.md and audit the common failures that make a high-AUC paper look untrustworthy.
6. Review each dimension — record Issue | Severity (Blocker/Major/Minor) | Location | Guideline/risk | Fix.
7. Run two-pass claim audit for submission-facing text — abstract, Key Results, figure legends, tables, graphical abstract, and Discussion comparison/novelty claims should be extracted first, then verified via references/claim-verification-gate.md.
8. Check claims vs evidence — abstract, Key Results, Discussion: is every claim bounded by the data?
9. Write the report (review-report-format.md) — reviewer comments by severity + an editor-style recommendation + a prioritised fix order (what unlocks the most).

Output contract

1. Summary assessment — 3–5 sentences: what the paper does, its real strength, its decisive weakness, and the readiness verdict.
2. Major/Blocker comments — numbered, reviewer-style, each with location, the guideline/risk, and the concrete fix.
3. Minor comments — numbered, smaller issues.
4. Claims vs evidence — overclaims and the bounded rewording.
5. Claim audit status — for final readiness: extraction complete? verification complete? unsupported/numerical/visual-table claims remaining?
6. Hard-gate table — if final readiness is requested: contribution, data integrity, validation, statistics, reporting, figures, citation, ethics/data availability, and reviewer objection status.
7. Editor-style recommendation — ready / minor revision / major revision / not yet, with reasons.
8. Fix order — prioritised, routed to the relevant skill (stats, reporting, design, etc.).

Quality bar

A good mock review predicts the real reviews: it catches the dealbreakers, cites the exact item a reviewer would, separates fatal from cosmetic, and tells the author the order to fix things — without inventing compliance or softening a genuine blocker.

Handoffs

• Checklist item-by-item audit → radiology-reporting.
• Statistical completeness (CIs, calibration, DCA, multiplicity) → radiology-stats.
• Leakage specifics → radiology-radiomics / radiology-deep-learning.
• Missing external validation / reader study → radiology-design / radiology-translation.
• Data/code/ethics gaps → radiology-data / radiology-ethics.
• Rewriting overclaims / sections → radiology-writing / radiology-polishing.
• Then choose the venue → radiology-journal; reviewer replies later → radiology-response.