Mapping a competency-based surgical curriculum in urology: Agreement (and discrepancies) in the Canadian national opinion
Bibliographic record
Abstract
INTRODUCTION: Urology residency training in Canada is quickly evolving from a time-based to a competency-based model. We aim to better define core surgical competencies that would comprise a surgical curriculum and assess any discrepancies in opinion nationally. METHODS: A web-based survey was validated and sent to the 536 practicing members of the Canadian Urological Association (CUA) in August and October 2014. The survey consisted of questions regarding practice demographics, fellowship training, and evaluated the 76 most common urological procedures (using a five-point Likert scale) in the context of the question, "After completion of residency training in Canada a urologist should be proficient in…" A core procedure was defined as one for which there was ≥75% agreement. Descriptive statistics and non-parametric testing were used to summarize the findings. RESULTS: A total of 138 urologists completed the survey (25.7% response rate) with representation from all geographic regions. Respondents included 40.6% community and 59.4% academic urologists. The survey identified 16 procedures with 90-100% agreement and a total of 30 core procedures with ≥75% agreement. When comparing community and academic urologists, there was statistically significant disagreement on 27 procedures, including 11 core procedures, most notably cystectomy (88.5% agreement vs. 67.1%; p=0.002), open pyeloplasty (84.6% vs. 65.8%; p=0.04), simple prostatectomy (78.9% vs. 69.7%; p=0.03), perineal urethrostomy (80.8% vs. 67.1%; p=0.02), open radical prostatectomy (96.1% vs. 80.3%; p=0.007), and Boari flap (90.4% vs. 76.3%; p=0.004). Regional discrepancies were also found, demonstrating eight procedures deemed uniquely core and three core procedures deemed less important regionally. CONCLUSIONS: This national survey has provided some consensus on 30 procedures that should comprise a core surgical curriculum in urology. However, there are some key differences of opinion (most notably between community and academic urologists) that must be considered.
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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 | 0.031 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| 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".