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![]() ![]() With a new enhancement, clinicians can now earn AMA PRA Category 1TM CME credits by simply reading the clinical content for 150+ calculators. ![]() ![]() The simple yet sleek app provides access to 550+ easy-to-use clinical decision tools including risk scores, algorithms, equations, formulas, classifications, dosing calculators, and more. MDCalc clinical decision support is created exclusively by board-certified physicians for use by physicians, physician assistants, nurse practitioners, pharmacists, and medical students. Registration is free and takes less than 30 seconds for full, unlimited access. Since 2005, MDCalc has been the leading medical reference for the most relevant, up-to-date and widely-used clinical calculators that support evidence-based patient care. Specificity: probability that a test result will be negative when the disease is not present (true negative rate).Sensitivity: probability that a test result will be positive when the disease is present (true positive rate).(*) These values are dependent on disease prevalence.Join the millions of medical professionals who use MDCalc daily to support clinical decision making at the bedside. ![]() = Sensitivity × Prevalence + Specificity × (1 − Prevalence) Accuracy: overall probability that a patient is correctly classified.Negative predictive value: probability that the disease is not present when the test is negative.Positive predictive value: probability that the disease is present when the test is positive.= False negative rate / True negative rate = (1-Sensitivity) / Specificity Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the disease and the probability of a negative test result given the absence of the disease, i.e.= True positive rate / False positive rate = Sensitivity / (1-Specificity) Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the disease and the probability of a positive test result given the absence of the disease, i.e. ![]()
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