Receiver Operating Characteristic (ROC) Curves

Earn 12.5 AMA PRA Category 1 Credits™ with the Core EM Modular CME Course. 12 curated EM modules with audio-first learning plus quizzes and downloadable resources. Start here: https://www.highmarksce.com/nyumc/Planners/viewActivity?style=2&preview=true&plannerID=3560#CourseDetails — In this Making Evidence-Based Medicine Simple (MESS Mini) episode, we explore how Receiver Operating Characteristic (ROC) curves help clinicians evaluate diagnostic tests that produce a range of results, not just “positive” or “negative” outcomes. Using the example of white blood cell counts in appendicitis, the episode demonstrates how ROC curves illustrate the tradeoff between sensitivity (true positive rate) and specificity (true negative rate) to determine a test’s overall accuracy. The video breaks down how the area under the curve (AUC) quantifies test performance—from worthless (AUC 0.5) to excellent (AUC 0.9+)—and shows how comparing multiple curves (e.g., WBC vs CRP) reveals which test performs better. It also explains how to identify the optimal cutoff point—the “inflection” where gains in sensitivity start to cause sharp losses in specificity. By the end, viewers will understand: • When to use interval likelihood ratios versus ROC curves. • How to interpret the AUC as a measure of diagnostic accuracy. • How ROC curves help determine the best threshold for clinical decision-making. A concise, visual, and practical walkthrough of one of the most essential tools in evidence-based diagnostic reasoning