A Pilot Study on Diagnostic Radiology Residency Case Volumes From a Canadian Perspective: A Marker of Resident Knowledge
Bibliographic record
Abstract
PURPOSE: New guidelines from the Accreditation Council for Graduate Medical Education (ACGME) have proposed minimum case volumes to be obtained during residency. While radiology residency programs in Canada are accredited by the Royal College of Physicians and Surgeons of Canada, there are currently no minimum case volumes standards for radiology residency training in Canada. New changes in residency training throughout Canada are coming in the form of competency-based medical education. Using data from a pilot study, this article examines radiology resident case volumes among recently graduated cohorts of residents and determines whether there is a correlation between case volumes and measures of resident success. MATERIALS AND METHODS: Resident case volumes for 3 cohorts of graduated residents (2016-2018) were extracted from the institutional database. Achievement of minimum case volumes based on the ACGME guidelines was performed for each resident. Pearson correlation analysis (n = 9) was performed to examine the relationships between resident case volumes and markers of resident success including residents' relative knowledge ranking and their American College of Radiology (ACR) in-training exam scores. RESULTS: < .05). CONCLUSIONS: This study suggests that residents who interpret more cases are more likely to demonstrate higher knowledge, thereby highlighting the utility of case volumes as a prognostic marker of resident success. As well, the results underscore the potential use of ACGME minimum case volumes as a prognostic marker. These findings can inform future curriculum planning and development in radiology residency training programs.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".