True or False: Bayes's theorem in clinical decision-making uses pretest probability and likelihood ratio to determine posttest probability.

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The main idea being tested is that Bayes' theorem in clinical decision-making updates a clinician’s initial probability of disease (the pretest probability) using the test’s likelihood ratio to arrive at a posttest probability. In practice, you start with a pretest probability based on history, exam, and risk factors. Then you apply the test’s likelihood ratio, which blends the test’s sensitivity and specificity, to shift that probability. If the test is positive, you use the positive likelihood ratio; if negative, you use the negative likelihood ratio. By converting the pretest probability to odds, multiplying by the appropriate likelihood ratio, and converting back to probability, you obtain the posttest probability. Pretest probability is not the same as population prevalence, and posttest probability cannot be determined from prevalence or test accuracy alone without incorporating that prior probability and the likelihood ratio. For example, a moderate pretest probability combined with a strong positive likelihood ratio can raise the posttest probability substantially, guiding management decisions.