Status of robotic-assisted surgery among Canadian urology residents
Bibliographic record
Abstract
BACKGROUND: Robotic-assisted surgery (RAS) has been rapidly adopted in urology, especially in the United States. Although less prevalent in Canada, RAS is a growing and controversial field that has implications for resident training. We report on the status and perception of RAS among Canadian urology residents. METHODS: : All Canadian urology residents from anglophone programs were contacted by email and asked to participate in an online survey. Current resident exposure to, and perception of, RAS was assessed. RESULTS: : Of the residents contacted (n = 128), 50 (39%) completed the survey. Of the respondents, 52% have been involved in RAS. Those who have not been involved in RAS express lower interest and lesser knowledge of RAS. Ninety-two percent of respondents feel the use of RAS will increase, although only 29% feel this is feasible in Canada. Just 24% and 36% feel RAS to be superior to open and laparoscopic techniques, respectively. Sixty-eight percent of residents in programs with a robot viewed it as detrimental to training, whereas 81% of residents in programs without one viewed its absence to either have no impact, or even be beneficial. Both groups expressed a desire for more experience with RAS. CONCLUSION: : The resident experience with respect to RAS is mixed. Overall, residents view RAS as an expanding field with potentially negative impacts on their present training, although they appear to desire the acquisition of more experience in RAS. We plan to monitor the evolution of these perceptions over next four years.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".