Evaluating the perceptions of Canadian urology residents and program directors regarding the current training in genitourinary imaging
Bibliographic record
Abstract
INTRODUCTION: Competency in interpreting genitourinary (GU) imaging is an important skill for urologists; however, no nationally accredited GU imaging curriculum exists for Canadian urology residency training programs. The main objectives of our study were to 1) characterize GU imaging training in Canada; (2) evaluate residents' self-perceived competencies in interpreting GU imaging; (3) explore program directors' (PD) and residents' perceptions regarding the current imaging curriculum and suggestions for future directions. METHODS: From November to December 2022, a survey examining current imaging education in residency, perceived resident imaging knowledge, avenues for improvement in imaging education, and the role of point-of-care ultrasound within urology was distributed to all Canadian urology PDs and residents. RESULTS: All PDs (13/13) and 40% (72/178) of residents completed the survey. Only two programs had a formal GU imaging curriculum. PDs and residents reported trainees were least comfortable interpreting Doppler ultrasound of renal, gonadal, and penile vessels. PDs reported that residents were most comfortable with non-contrast computed tomography (CT) scans (9.5/10), CT urogram (9.3/10), and retrograde pyelography (9.3/10). All but one PD favored increasing imaging training in their program. PDs highlighted the lack of time in the curriculum (n=3) and lack of educators (n=3) as the primary barriers to increasing imaging training in their program. CONCLUSIONS: Most PDs and residents believe there needs to be more imaging training offered at their institution; however, addressing this is challenging due to the limited time in the curriculum and the need for available educators.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".