Prevalence of abnormal cases in an image bank affects the learning of radiograph interpretation
Bibliographic record
Abstract
OBJECTIVES: Using a large image bank, we systematically examined how the use of different ratios of abnormal to normal cases affects trainee learning. METHODS: 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. RESULTS: 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. CONCLUSIONS: Manipulating the ratio of abnormal to normal cases in learning banks can have important educational implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".