A researcher constructs a ROC curve for a new biomarker to diagnose early sepsis. The area under the ROC curve (AUC) is 0.92. Which statement BEST interprets this result?
- A The test correctly classifies 92% of diseased patients as positive
- B The sensitivity of the test is 92% at the optimal threshold
- C There is a 92% probability that the test score of a randomly chosen diseased person exceeds that of a randomly chosen non-diseased person ✓
- D The specificity of the test is 92% at the optimal threshold
Correct answer: C. There is a 92% probability that the test score of a randomly chosen diseased person exceeds that of a randomly chosen non-diseased person
Explanation
AUC-ROC equals the probability that the classifier ranks a randomly chosen positive instance higher than a randomly chosen negative instance (Wilcoxon-Mann-Whitney statistic interpretation). It summarises overall discriminative ability across all thresholds, not at any single cut-off. Sensitivity and specificity values depend on the chosen threshold, not on the AUC itself.
Reference: Park's Textbook of Preventive and Social Medicine, 27th ed.
High-yield for: NEET PGINI-CETNExTFMGEUSMLEPLABMRCP
Written and medically reviewed by the StethoPrep medical team.