What term describes the measure of how well a test can identify true positives?

The term that describes the measure of how well a test can identify true positives is sensitivity. Sensitivity, also known as the true positive rate, quantifies the proportion of actual positive cases correctly identified by the test. It is calculated by taking the number of true positives and dividing it by the sum of true positives and false negatives.

In practical terms, high sensitivity means that the test is effective at detecting true cases of a disease or condition, minimizing the chances of falsely classifying someone with the condition as healthy. This is particularly important in healthcare settings where failing to identify a condition (such as a disease) could lead to worsened health outcomes.

The other choices do not measure the ability to identify true positives. Specificity pertains to the test's ability to identify true negatives, predictive value refers to the probability that subjects with a positive (or negative) test truly have the disease (or do not have it), and accuracy measures the proportion of total correct predictions (both true positives and true negatives) among the total number of cases examined. Understanding sensitivity is vital for evaluating the effectiveness of screening tests and diagnostic tools in healthcare.