design-study
FeaturedStudy 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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Quality Score: 95/100
Skill Content
Details
- Author
- Aperivue
- Repository
- Aperivue/medsci-skills
- Created
- 3 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
Bundled in these plugins
Similar Skills
Semantically similar based on skill content — not just same category
design-ai-benchmarking
Design and validity review for studies that benchmark one or more AI systems against a human-expert panel as the reference. Covers the evaluation question and arm definition, decoupled multi-dimensional rubrics with anchors, planted calibration probes, reviewer-panel construction, inter-rater reliability targets, LLM-as-judge versus human-as-judge adjudication, construct-independence guards, and a structured rating-export schema. Use before data collection on an AI-vs-expert evaluation.
model-validation
Design or audit the clinical-validation study for an engineer-built medical-imaging model (segmentation, classification, or detection) before the validation report or manuscript is written. Covers patient-level split disjointness and the data-leakage taxonomy, tuning-on-test, internal versus genuine external validation, comparator design, single-run versus multi-seed variance, task-correct metric selection, test-set sizing, and CLAIM 2024 / TRIPOD+AI / STARD-AI reporting fit. Ships a deterministic split-leakage gate that proves patient disjointness by set arithmetic on the emitted split-assignment table. Does not build or train models — it integrates with MONAI / nnU-Net, it does not replace them.
check-reporting
Check manuscript compliance with medical research reporting guidelines. Supports 47 guidelines including STROBE, STROBE-MR, RECORD, REMARK (prognostic tumor-marker studies), TARGET (target trial emulation), GATHER (burden-of-disease / health-estimate modeling), CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR (scoping reviews), CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, CHEERS 2022, CROSS (survey studies), SRQR and COREQ (qualitative research), and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.