Can American College of Radiology in-training examination scores be used to predict Canadian radiology licensing examination results? A retrospective study
Bibliographic record
Abstract
BACKGROUND: The purpose of this study is to evaluate the relationship between American College of Radiology (ACR) in-training examination scores and performance on the Royal College of Physicians and Surgeons of Canada (RCPSC) radiology licensing examination. METHODS: Percentile ACR examination scores for 67 residents were obtained from 1995 to 2011 for four years of training and compared with results of the RCPSC examination. Mean ACR scores of residents who passed and residents who failed their RCPSC examination were compared with a t-test. ACR scores and licensing examination results were correlated. Logistic regression was used to predict the probability of failure given an individual's ACR score. Receiver Operating Characteristic (ROC) curves were developed in order to estimate a threshold ACR score at or above which the risk of failure was negligible. RESULTS: The ACR scores between residents who passed their licensing exam and those who failed were significantly different. There was moderate correlation between ACR scores and exam results. Using ROC curves for each year of training, the threshold ACR scores at or above which there was a negligible risk of exam failure were 32, 42, 63, and 47 for training years 1, 2, 3 and 4 respectively. Logistic regression curves, with 95% confidence intervals, were plotted for each year of training to predict RCPSC exam results based on an individual's ACR score. CONCLUSIONS: ACR exam scores are a strong predictor of RCPSC examination performance. Percentile ACR scores can be used to identify residents at risk for future examination failure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".