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.
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.
Final submission readiness audit, rejected-paper rescue, contribution map, reviewer objection register, or when deciding whether a paper is truly ready
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
Submission-facing abstract, Key Results, figure legend, table, graphical abstract, novelty, comparison, and numerical claims need two-pass extraction and verification
Workflow
Intake — manuscript (or sections), study type, target journal/tier if known.
Classify the study and load the dimensions (review-dimensions.md); pull the right
guideline stack via radiology-reporting.
For final readiness checks, open pre-submission-hard-gates.md and score each hard
gate as PASS / CONDITIONAL / FAIL before writing softer reviewer comments.
For AI/radiogenomics manuscripts, open ai-radiogenomics-pitfall-audit.md and audit
the common failures that make a high-AUC paper look untrustworthy.
Review each dimension — record Issue | Severity (Blocker/Major/Minor) | Location | Guideline/risk | Fix.
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.
Check claims vs evidence — abstract, Key Results, Discussion: is every claim bounded by the
data?
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
Summary assessment — 3–5 sentences: what the paper does, its real strength, its decisive
weakness, and the readiness verdict.
Major/Blocker comments — numbered, reviewer-style, each with location, the guideline/risk,
and the concrete fix.
Minor comments — numbered, smaller issues.
Claims vs evidence — overclaims and the bounded rewording.
Claim audit status — for final readiness: extraction complete? verification complete?
unsupported/numerical/visual-table claims remaining?
Hard-gate table — if final readiness is requested: contribution, data integrity,
validation, statistics, reporting, figures, citation, ethics/data availability, and reviewer
objection status.
Editor-style recommendation — ready / minor revision / major revision / not yet, with
reasons.
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.
