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Study design and validity review for radiology and medical AI research. Identifies analysis unit, cohort logic, leakage risks, comparator design, validation strategy, and reporting guideline fit before drafting or submission.
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Peer review assistant for medical journals. Generates structured review drafts with journal-specific formatting. Constructive developmental tone with systematic manuscript analysis.
Interactive data profiling and cleaning assistant for medical research. Three-stage workflow (profile, flag, code-generate) with user approval gates at each step. Handles missing values, outliers, duplicates, and type mismatches in CSV/Excel clinical data. Does NOT auto-clean — all decisions require researcher confirmation.
Journal recommendation engine for medical manuscripts. 2-pass matching against a curated public profile library plus any user-local private profiles, enriched with detailed write-paper profiles for top-5 output. Returns ranked recommendations with scope fit rationale, AI disclosure policy, and homepage links. Impact-factor and APC figures in a profile are point-in-time and may be stale — verify current metrics at the journal site. A pre-ranking acceptance-readiness pre-flight scans the manuscript for design-ceiling, unfixable-defect, and importance-risk signals to add an acceptance-feasibility axis alongside scope fit, and the output includes a reject-fallback cascade plan.
SOC 職業分類に基づく
SKILL.md を表示中
| name | design-study |
| description | Study design and validity review for radiology and medical AI research. Identifies analysis unit, cohort logic, leakage risks, comparator design, validation strategy, and reporting guideline fit before drafting or submission. |
| triggers | study design, leakage check, cohort design, analysis plan, validation strategy, comparator design, bias check |
| tools | Read, Write, Edit, Bash, Grep, Glob |
| model | inherit |
This skill pressure-tests whether a study is answerable, interpretable, and defensible before large amounts of drafting or analysis work accumulate.
Use it when:
Always inspect these dimensions:
/define-variables before drafting Methods.## Study Design Review
Question: ...
Study type: ...
Analysis unit: ...
Index date / prediction timepoint: ...
### Strengths
- ...
### Major validity risks
1. ...
2. ...
### Minimal fixes
- ...
### Reporting fit
- Recommended guideline: ...
### Decision
- Ready for analysis / Needs redesign / Drafting can proceed with limitations
Extract from protocol, draft, slides, tables, or notes:
Gate: Present the reconstructed study summary (question, analysis unit, intended use) to the user. Confirm before proceeding — if the reconstruction is wrong, the entire validity review will be misdirected.
Look for mismatches such as:
Look for:
For any time-to-event or incident/transition design, check before drafting:
Check:
variable_operationalization.md. The variable definitions written in Methods must match the operationalization table verbatim (dictionary-first). A blinded re-classification form must quote the analytic protocol's definition verbatim — paraphrase / "common-sense extension" in the form (but not the Methods) is the documented cause of a low κ that is a definition mismatch, not real disagreement. Cross-check with /define-variables output before drafting.Classify:
When the study elicits expert ratings — a reader study, an annotation panel, an AI-output evaluation — the design decisions that matter are made before data collection, and the acceptance ceiling of a perceptual / reader AI study is fixed at design time: no quality of execution lifts a ceiling baked into the comparator, the estimand, or the reader cohort.
For an AI-system-versus-human-expert benchmark specifically, route to /design-ai-benchmarking,
which extends this subsection with arm definition, LLM-as-judge versus human-as-judge
adjudication, and a structured export schema.
| File | Read it when | Cost if read blindly |
|---|---|---|
references/reader_elicitation_design.md | the design has a human-rater or expert-elicitation arm — rubric axes, calibration probes, operational rigor, human-as-operator, and the six ceiling decisions | ~2,400 tokens, none of which applies to a design with no reader arm |
references/dag_adjustment.md | confounding control needs an explicit adjustment set | — |
references/target_trial_emulation.md | the design emulates a target trial | — |
references/venue_accept_recipe.md | it is a clinical DL / AI-validation study and the question is which venue tier the achievable design can be accepted at, and the one design move that reaches the tier above (the design→acceptance-tier ladder + the five acceptance levers, reverse-engineered from accepted OA papers; the bridge into /find-journal) | ~1,800 tokens; skip for a design with no publication-tier decision |
references/combine_models_ablation_design.md | the model is built by combining / adapting / fine-tuning existing models (nnU-Net, TotalSegmentator, SAM/MedSAM, a pretrained backbone) — how to design the comparator as an ablation that proves the combination earns its complexity (beat the un-adapted base + the best single component + direct-train), reverse-engineered from accepted OA papers | ~1,600 tokens; skip if the model is trained de novo with no reused component |
references/multi_model_comparison_design.md | the study's contribution is comparing several models / architectures head-to-head (CNN vs Transformer vs foundation backbone; N segmentation networks) — how to make the comparison fair: one frozen split + one preprocessing through every model, a strong self-configuring baseline (nnU-Net) not a hobbled one, matched training/HPO/compute budget (the #1 threat — "new≠better, just tuned harder") or disclosed, variance-over-single-run, a pre-specified primary metric + a paired delta test, honest ranking. Reverse-engineered from accepted OA papers | ~1,700 tokens; skip for a single-model study (use combine_models_ablation_design.md for an ablation of one model, /design-ai-benchmarking for AI-vs-human) |
references/segmentation_failure_characterization_design.md | the claim is that a segmentation model is clinically usable, not that it scores well — a pre-specified failure taxonomy (boundary drift / missed / hallucinated / catastrophic), an acceptability endpoint with a stated scale, named judges and an adjudication rule, the tail (per-case distribution, worst percentile, catastrophic count) beside the mean, edit effort paired against manual-from-scratch and disaggregated per structure and site, and failures stratified by what predicts them. Reverse-engineered from accepted OA papers | ~1,700 tokens; skip when the endpoint is benchmark accuracy with no usability claim (metric choice → /model-evaluation; abstention / risk–coverage → /uncertainty-imaging) |
Ask whether the comparator and endpoint support the stated claim:
references/combine_models_ablation_design.md. When the contribution is instead a head-to-head comparison of several models (which architecture wins), the decisive design question is comparison fairness — one frozen split/preprocessing through every model, a strong fairly-tuned baseline, a matched (or disclosed) compute budget, and a paired delta test — per references/multi_model_comparison_design.md. When the claim is not that a segmentation model scores well but that it is clinically usable, the design must carry a pre-specified failure taxonomy, an acceptability endpoint with a named judge and adjudication rule, the tail beside the mean, and edit effort paired against manual-from-scratch — per references/segmentation_failure_characterization_design.md; a mean DSC cannot be converted into a usability claim after the fact./self-review §D + check_scope_coherence.py flag CROSS_SECTIONAL_PROGNOSTIC / SURROGATE_CARE_DIRECTIVE against the conclusion.Recommend one primary guideline:
TRIPOD-AICLAIMSTARDSTROBEPRISMACAREARRIVEreferences/target_trial_emulation.md.scripts/adjustment_set_helper.py (flags mediator / collider / descendant adjustment and omitted confounders, and proposes a candidate backdoor set), then derive the minimal sufficient set with dagitty — see references/dag_adjustment.md. At review time /self-review Phase 2.5e + the O1–O12 probes in observational_confounding.md check this against Table 1 (including O7 over-adjustment, O10 overlapping-subset-gradient discipline, for complex-survey data O11 design-based weighting and O12 data-driven-threshold mining, O13 — a cross-sectional mediation claim cannot order X→M→Y, and O14 — a synergy/joint-effect claim needs the additive interaction scale (RERI/AP/S), not a multiplicative-only test).Whenever possible, recommend the smallest feasible repair first:
Escalate to redesign only when the central claim is not defensible otherwise.
analyze-stats when the design is basically sound but analysis details need refinementcheck-reporting after the design is lockedself-review when the user wants a pre-submission quality check on their own manuscriptwrite-paper only after the main validity risks are documented/search-lit with confirmed DOI or PMID. Mark unverified references as [UNVERIFIED - NEEDS MANUAL CHECK].[VERIFY] and ask the user.