radiology-stats
radiology-stats is a Statistical modelling skill for AI agents, published by huang-sir1 in the radiology-skills repository on GitHub. Skillspace rates radiology-stats C (71/100) and it passed the safety scan; the rating is based on static analysis only.
Plan, run, and report imaging biostatistics the way Radiology (RSNA) reviewers expect — diagnostic accuracy with CIs, ROC/AUC comparison (DeLong/bootstrap), reader agreement (Cohen/Fleiss kappa, ICC, Bland-Altman), multi-reader multi-case (MRMC) studies, calibration and decision-curve analysis, multiplicity control for high-dimensional radiomics/omics (Bonferroni vs FDR), survival/prognostic modelling, and sample-size/EPV planning. Use when the user mentions AUC, DeLong, sensitivity/specificity, kappa, ICC, MRMC, calibration, decision curve, p-value, FDR, multiple comparisons, sample size, C-index, or asks how to report a statistic for Radiology. Provides runnable Python/R and a results sentence; never fabricates numbers.
- Grade
- C
- Quality
- 71 / 100
- Safety
- Passed
- GitHub stars
- 819
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 94 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
- Scope
- Portable: works in any project. General imaging biostatistics guidance for any researcher
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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