A patient with suspected autoimmune thyroiditis undergoes a diagnostic test. The test correctly identifies 85% of individuals without the condition. Which of the following terms best describes the probability that a negative test result truly indicates the absence of autoimmune thyroiditis?
B) Negative predictive value
Negative predictive value is the probability that a negative test result truly indicates the absence of the disease. It is calculated as true negatives/(true negatives + false negatives). In this case, it represents the likelihood that a negative test accurately indicates the absence of autoimmune thyroiditis in the patient with suspected symptoms.
Answer choice A: False negative rate, is incorrect. The false negative rate is the proportion of individuals with the disease who receive a negative test result. It is calculated as false negatives/(false negatives + true positives).
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 D: Sensitivity, is incorrect. Sensitivity is the probability of a positive test result when the disease is present. It is calculated as true positives/(true positives + false negatives).
Answer choice E: Specificity, is incorrect. Specificity is the probability of a negative test result when the disease is absent. It is calculated as true negatives/(true negatives + false positives).
Key Learning Point
Negative predictive value is the probability that a negative test result truly indicates the absence of the disease. It is calculated as true negatives/(true negatives + false negatives). Negative predictive value is essential for understanding the reliability of a negative test result in ruling out a particular condition in a given patient.
