tooluniverse-diagnostic-test-evaluation

name	tooluniverse-diagnostic-test-evaluation
description	Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
disable-model-invocation	true

Diagnostic Test / Biomarker Accuracy Evaluation

Judge how well a test or biomarker discriminates disease — at a fixed cutoff (2×2) or across all cutoffs (ROC) — and turn a result into a probability of disease.

Which case are you in?

You have…	Go to
A 2×2 table (TP/FP/TN/FN) at a fixed cutoff	Step 1 (Epidemiology_diagnostic)
A continuous biomarker score + true labels	Step 2 (ROC / AUC / Youden, Python)
A test's sens/spec + a patient's pre-test probability	Step 3 (Epidemiology_bayesian)

Step 1 — Fixed-cutoff metrics from a 2×2 table

tu run Epidemiology_diagnostic '{"operation":"diagnostic","tp":90,"fp":10,"tn":180,"fn":20}'

Returns sensitivity, specificity, , , , , , and the sample .