TRIPOD Desk - a clinical prediction model report in, a submission-ready validation review out

Paste the methods and results of a clinical prediction model, plus the table of what it scored on each split, and find out whether it is reportable. A free in-browser read checks forty-seven TRIPOD+AI items in three states, computes events per candidate predictor and the outcome prevalence, screens every predictor against your own stated time of prediction, and checks each metrics row against itself - the interval against its own estimate, the predictive values against sensitivity, specificity and prevalence by Bayes' rule, the Brier score against the base rate. Then four lanes work the report: cohort and leakage, validation and performance, attribution claims, and the TRIPOD+AI reporting pack with a drafted abstract, limitations and intended-use statement. A derived work crediting @k-dense-ai/pyhealth, @k-dense-ai/scikit-survival, @k-dense-ai/scikit-learn, @k-dense-ai/statistical-analysis and @k-dense-ai/shap. Not a clinical device.

Every public app is built from a security-scanned skill and must pass a clean scan — skill and frontend — before it can be listed. Have a skill of your own? Turn it into an app — or read the step-by-step walkthrough.