## Definition of Sensitivity **Key Point:** Sensitivity is the ability of a test to correctly identify those WITH the disease. ### Formula $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ Where: - TP = True Positives (correctly identified disease cases) - FN = False Negatives (missed cases) ### Interpretation A sensitivity of 95% means that if 100 people have the disease, the test will correctly identify 95 of them as positive. In other words, **95% of people with the disease will test positive**. ### Clinical Significance **High-Yield:** Sensitivity answers the question: "If someone HAS the disease, what is the probability the test will be POSITIVE?" **Clinical Pearl:** High-sensitivity tests are used for **screening** and **ruling OUT disease** (SnNout — Sensitivity, Negative, rule out). A negative result from a highly sensitive test effectively excludes the disease. ### Contrast with Specificity Specificity (90% in this case) tells us that 90% of people WITHOUT the disease will test negative — it is about correctly identifying those WITHOUT disease. **Warning:** Do NOT confuse sensitivity with positive predictive value (PPV). PPV tells you the probability that a positive test is truly positive — that depends on disease prevalence, not just test characteristics.
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