Survey of senior resident training in urologic laparoscopy, robotics and endourology surgery in Canada
Bibliographic record
Abstract
INTRODUCTION: We determined the status of Canadian training during senior residency in laparoscopic, robotic and endourologic surgery. METHODS: Fifty-six residents in their final year of urology residency training were surveyed in person in 2007 or 2008. RESULTS: All residents completed the survey. Most residents (85.7%) train at centres performing more than 50 laparoscopic procedures yearly and almost all (96.4%) believe laparoscopic radical nephrectomy is the gold standard. About 82% of residents participated in a laparoscopic partial nephrectomy in 2008, compared to 64.7% in 2007. Of the respondents, 66% have participated in a laparoscopic prostatectomy and 54% believe the procedure has promising potential. Exposure and training in robotic-assisted laparoscopic procedures seem to be increasing as 35.7% of 2008 residents have access to a surgical robot and 7% consider themselves trained in robotic-assisted procedures. Most residents (71.4%) train at centres that perform percutaneous ablation. However, 65% state the procedure is performed solely by radiologists. Percutaneous nephrolithotomy is widely performed (98.2%), but only 37.5% of residents report training in obtaining primary percutaneous renal access. Despite only 12.5% of residents ranking their laparoscopic experience as below average or poor, an increasing proportion of graduating residents are pursuing fellowships in minimally-invasive urology. CONCLUSION: Laparoscopic nephrectomy is commonly performed and is considered the standard of care by Canadian urology residents. Robotic-assisted surgery is becoming more common but will require continued evaluation by educators who will ultimately define its role in the urological residency training curriculum. Minimally-invasive surgical fellowships remain popular, as Canadian residents do not feel adequately trained in certain advanced procedures. Urologists must strive to learn and adapt to new technologies or risk losing them to other specialties.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".