Resident Case Volumes Following the Introduction of Competency-Based Medical Education in Diagnostic Radiology: A 10 Year Review
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: A Canadian Diagnostic Radiology residency program implemented a competency-based medical education (CBME) curriculum in 2017 (Q-CBME), followed by the national Competence by Design (CBD) model in 2022. These frameworks emphasize progression through frequent assessment of competencies rather than time in training. However, concerns remain that the time burden or "tick-box" mentality in the context of increased assessments may decrease clinical exposure. This study evaluated whether CBME implementation affected resident clinical case volumes compared to a traditional time-based curriculum. MATERIALS AND METHODS: Using Nuance mPower Clinical Analytics, case volumes were extracted for residents in traditional (n=9), Q-CBME (n=16), and CBD (n=5) curricula during their first abdominal, chest, and neuroimaging rotations. Volumes were normalized by days worked and averaged by cohort. A secondary analysis compared total volumes between traditional (n=8) and Q-CBME (n=12) residents over the entirety of residency. Comparisons were made using ANOVA and independent t-tests. RESULTS: ANOVA revealed significant difference in abdominal CT volumes (p=0.0002). T-tests showed significant difference for neuroradiology CT (traditional vs CBD, p=0.0008), abdominal CT (traditional vs Q-CBME, p=0.0015; traditional vs CBD, p=0.0005), and chest X-ray (Q-CBME vs CBD, p=0.0262). Whole-residency analysis found significant differences only in MRI (p=0.0195) and nuclear medicine (p=0.0003). In all significant differences between a CBME and traditional cohort, Q-CBME and CBD had higher mean volumes. CONCLUSION: CBME offers opportunities for enhanced feedback and mentorship. This study found no reduction in resident case volumes under CBME, supporting the view that competency-based training can maintain, or even increase, clinical exposure. Given prior findings linking case volume to competency, these results support continued CBME adoption in radiology training.
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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 | 0.006 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".