## Definition of Sensitivity **Key Point:** Sensitivity is the ability of a test to correctly identify individuals WITH the disease. It answers the question: "If someone has the disease, what is the probability the test will be positive?" ### Mathematical Formula $$\text{Sensitivity} = \frac{TP}{TP + FN}$$ Where: - TP = True Positives (correctly identified diseased individuals) - FN = False Negatives (diseased individuals who tested negative) ### Clinical Interpretation A sensitivity of 95% means that if 100 patients with TB are tested, 95 will test positive and 5 will be missed (false negatives). **High-Yield:** Sensitivity is a **test characteristic** — it does NOT depend on disease prevalence and remains constant across populations. It is used to evaluate how good a test is at **ruling out disease** (high sensitivity = good negative predictive value). ### Memory Aid **Mnemonic:** **SNout** — **S**ensitivity and **N**egative test rule **out** disease. A highly sensitive test with a negative result effectively excludes the diagnosis. ### Why Other Options Are Wrong - Option 1 (correct): Directly matches the definition of sensitivity - Option 2: This describes **specificity** — the ability to correctly identify those WITHOUT disease - Option 3: This describes **Positive Predictive Value (PPV)** — depends on prevalence - Option 4: This describes **Negative Predictive Value (NPV)** — also depends on prevalence [cite:Park 26e Ch 10]
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