UroBOT: A national survey of Canadian urology residents and fellows on robot-assisted surgery
Bibliographic record
Abstract
INTRODUCTION: Robot-assisted surgery (RAS) has a positive impact on the quality of care given to patients. Its increasing adoption in Canadian urology practice also influences the surgical training of residents and fellows. Currently, the lack of clear objectives makes RAS education challenging. The main objective of our study was to highlight how urology trainees perceive the importance of RAS and the standardization of its training. METHODS: In 2021, we conducted a survey of all the residents and fellows enrolled in a Canadian urology program. The questions assessed their opinion on the importance of RAS and on their robotic surgery training. RESULTS: The response rate was 29%. The majority of participants (67%) wished they would have a better exposure to RAS during their surgical training. Only 7% of respondents reported that their program had clear criteria to help them progress through the steps of RAS, and most trainees (81%) felt their residency program should provide them with a formal RAS training program. Seventy-six percent of respondents believed that RAS would become a core skill required by the Royal College in the future, although 32% feared it would hinder their ability to learn other important techniques, such as open surgery. CONCLUSIONS: Our study revealed that although most respondents are interested in RAS, their training lacks standardization. Moreover, the potential integration of RAS as a core skill of the Royal College faces some important challenges, mostly due to the perceived lack of time to learn a new surgical technique.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".