Evolving attitudes toward robotic surgery among Canadian urology residents
Bibliographic record
Abstract
INTRODUCTION: Robotic-assisted laparoscopic surgery (RAS) has not been adopted as rapidly or widely in Canada as in the U.S. In 2011, Canadian urology residents felt that RAS represented an expanding field that could potentially negatively impact their training. We re-evaluate trainee exposure and attitudes to RAS in Canadian residency training five years later. METHODS: All Canadian urology residents were asked to participate in an online survey designed to assess current resident exposure to and perception of RAS. RESULTS: The response rate was 39% (61/157). Seventy-seven percent of residents reported being involved in at least one RAS procedure (52% in 2011), and the majority had exposure to <10 cases. For those in hospitals with access to RAS, 96% desired more console time, while only 50% of those without access wanted more console experience. Of all residents, 50% felt that RAS will become the gold standard in certain urological surgeries (34% in 2011), but only 28% felt that RAS would play an increasingly important role in urology (59% in 2011). CONCLUSIONS: Despite an increase in exposure to RAS in residency programs over the past five years, console experience remains limited. Although these residents desire more access to RAS, many voice uncertainty of the role of RAS in Canada. We cannot conclude whether RAS is perceived by residents to be beneficial or detrimental to their training nationwide. Moving forward in the robotic era, it will be important to either modify residency curricula to address RAS experience or to limit RAS to fellowship training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".