## Definition of Sensitivity **Key Point:** Sensitivity is the ability of a test to correctly identify those WITH the disease. It is the proportion of true positives among all patients who actually have the disease. ### Formula $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ Where: - TP = True Positives (patients with disease who test positive) - FN = False Negatives (patients with disease who test negative) ### Interpretation A sensitivity of 95% means that if 100 patients with TB are tested, 95 will test positive and 5 will test negative (false negatives). **High-Yield:** Sensitivity answers the question: "If a patient HAS the disease, what is the probability the test will be POSITIVE?" ### Clinical Pearl Sensitivity is crucial for **screening tests** — we want high sensitivity to avoid missing cases of serious disease. A test with low sensitivity will have many false negatives, allowing diseased patients to go undetected. ### Common Confusion - ~~Sensitivity = proportion of positive tests that are correct~~ (that is **Positive Predictive Value**, not sensitivity) - ~~Sensitivity = proportion of negative tests that are correct~~ (that is **Negative Predictive Value**) - ~~Sensitivity = proportion of disease-free people who test negative~~ (that is **Specificity**)
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