## Definition of Sensitivity **Key Point:** Sensitivity is the ability of a test to correctly identify individuals who have the disease. It answers the question: "If the disease is present, what is the probability the test will be positive?" ### Mathematical Formula Sensitivity = $\frac{TP}{TP+FN}$ = $\frac{\text{True Positives}}{\text{All Disease-Positive Individuals}}$ ### Clinical Interpretation In this TB example: - Sensitivity of 90% means that among 100 patients who actually have TB, the test will correctly identify 90 of them as positive. - The remaining 10 (false negatives) will incorrectly test negative despite having the disease. **High-Yield:** Sensitivity is a property of the test itself and does NOT depend on disease prevalence. It measures the test's ability to **rule out disease** — a highly sensitive test has few false negatives, making it useful as a screening test. ### What Sensitivity Is NOT - ~~Specificity~~ — that measures true negatives, not true positives - ~~Positive Predictive Value (PPV)~~ — that is the probability a positive test means disease is present (depends on prevalence) - ~~Negative Predictive Value (NPV)~~ — that is the probability a negative test means disease is absent
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