A patient with suspected infection undergoes a diagnostic test. The test correctly identifies 80% of individuals with the infection. Which of the following terms best describes the probability of a positive test result when the disease is present?
D) Sensitivity
Sensitivity is the probability of a positive test result when the disease is present. It is defined as true positives/(true positives + false negatives). In this case, sensitivity represents the ability of the test to correctly identify individuals with the infection.
Answer choice A: Accuracy, is incorrect. Accuracy is the overall correctness of the test and is calculated as (true positives + true negatives)/(total number of cases). While sensitivity is part of accuracy, accuracy considers both true positives and true negatives.
Answer choice B: Negative predictive value, is incorrect. Negative predictive valueis the probability that the disease is absent when the test is negative. It is calculated as true negatives/(true negatives + false negatives) or true negatives/(total number of negative tests).
Answer choice C: Positive predictive value, is incorrect. Positive predictive value is the probability that a positive test result truly indicates the presence of the disease. It is calculated as true positives/(true positives + false positives).
Answer choice E: Specificity, is incorrect. Specificity is the probability of a negative test result when the disease is absent. It is defined as true negatives/(true negatives + false positives).
Key Learning Point
Sensitivity is the probability of a positive test result when the disease is present. It is defined as true positive/(true positives + false negatives). Sensitivity is a crucial measure for evaluating the ability of a diagnostic test to correctly identify individuals with the disease.
