Sensitivity is defined as

Sensitivity is defined as

• A. Sensitivity = TP / (TP + FN)
• B. Specificity = TN / (TN + FP)
• C. PPV = TP / (TP + FP)
• D. NPV = TN / (TN + FN)

Answer: (A)

Sensitivity is the proportion of people who truly have the disease who are correctly identified by the test. It is calculated as true positives divided by the sum of true positives and false negatives (TP / [TP + FN]). This denominator includes everyone who actually has the condition, so sensitivity measures how good the test is at catching the disease and minimizing false negatives. High sensitivity is especially important in screening situations where you want to rule out disease if the test is negative (SnNout). For context, specificity is about correctly identifying those without disease (TN / [TN + FP]), while positive predictive value and negative predictive value involve how the test results relate to actual disease in the tested population (PPV = TP / [TP + FP], NPV = TN / [TN + FN]); PPV and NPV depend on disease prevalence, whereas sensitivity and specificity do not.

Sensitivity is the proportion of people who truly have the disease who are correctly identified by the test. It is calculated as true positives divided by the sum of true positives and false negatives (TP / [TP + FN]). This denominator includes everyone who actually has the condition, so sensitivity measures how good the test is at catching the disease and minimizing false negatives. High sensitivity is especially important in screening situations where you want to rule out disease if the test is negative (SnNout).

For context, specificity is about correctly identifying those without disease (TN / [TN + FP]), while positive predictive value and negative predictive value involve how the test results relate to actual disease in the tested population (PPV = TP / [TP + FP], NPV = TN / [TN + FN]); PPV and NPV depend on disease prevalence, whereas sensitivity and specificity do not.