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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: OBJECTIVE, METHODS, RESULTS. Meshes: Ankle, Clinical Competence, Decision Making, Diagnostic Errors, Double-Blind Method, Education, Medical, Undergraduate, Female, Humans, Male, Prospective Studies, Radiographic Image Interpretation, Computer-Assisted, Radiology, Sensitivity and Specificity, Students, Medical, Teaching.
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Does prevalence of abnormal cases in an image bank affect the learning of radiograph interpretation? Context: Using a large image bank, we systematically examined how the use of different ratios of abnormal to normal cases affects trainee learning. This was a prospective, double-blind, randomised, three-arm education trial conducted in six academic training programmes for emergency medicine and paediatric residents in post-licensure years 2-5. We developed a paediatric ankle trauma radiograph case bank. From this bank, we constructed three different 50-case training sets, which varied in their proportions of abnormal cases (30%, 50%, 70%). Levels of difficulty and diagnoses were similar across sets. We randomly assigned residents to complete one of the training sets. Users classified each case as normal or abnormal, specifying the locations of any abnormalities. They received immediate feedback. All participants completed the same 20-case post-test in which 40% of cases were abnormal. We determined participant sensitivity, specificity, likelihood ratio and signal detection parameters. A total of 100 residents completed the study. The groups did not differ in accuracy on the post-test (p = 0.20). However, they showed considerable variation in their sensitivity-specificity trade-off. The group that received a training set with a high proportion of abnormal cases achieved the best sensitivity (0.69, standard deviation [SD] = 0.24), whereas the groups that received training sets with medium and low proportions of abnormal cases demonstrated sensitivities of 0.63 (SD = 0.21) and 0.51 (SD = 0.24), respectively (p < 0.01). Conversely, the group with a low proportion of abnormal cases demonstrated the best specificity (0.83, SD = 0.10) compared with the groups with medium (0.70, SD = 0.15) and high (0.66, SD = 0.17) proportions of abnormal cases (p < 0.001). The group with a low proportion of abnormal cases had the highest false negative rate and missed fractures one-third more often than the groups that trained on higher proportions of abnormal cases.
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Manipulating the ratio of abnormal to normal cases in learning banks can have important educational implications. Final Decision: yes