Status of urologic laparoscopy in 2004: a survey of CUA members.
Bibliographic record
Abstract
INTRODUCTION: The optimal method of acquiring laparoscopic skills has not been determined. We sought to examine the current status of urologic laparoscopy and how practicing urologists acquired the skills needed to perform laparoscopic procedures. METHODS: A mail questionnaire regarding laparoscopic practices and training was sent to 480 members of the Canadian Urological Association (CUA) using standard Dillman survey methodology. RESULTS: Three hundred (62.5%) urologists responded to the questionnaire; 56.5% practiced in the community and 41.1% in an academic setting. There were 59.9% who had completed some form of fellowship training. Recent graduates (who finished residency after 1995) were more likely to perform all types of laparoscopic procedures compared to older graduates (65% versus 29.7%, p < 0.001). Advanced procedures were also performed more frequently by recent graduates (52.5% versus 23.4%, p < 0.001). Of those who do not currently perform laparoscopy, 38.2% plan to learn in the future. The most common method of acquiring laparoscopic skills was with animal laboratory experience (39.4%), but only 20.9% relied solely on this method. A trip to a centre of excellence (28.5%) and training from an urologist at the same institution (25.7 %) was also commonly reported as methods of acquiring skills. There were 48.8% who reported beginning laparoscopic procedures without a mentor. CONCLUSIONS: A substantial portion of the Canadian urological community employs laparoscopy, although recent graduates are more likely to do so. Training methods in laparoscopy are variable, but a substantial portion of urologists begin practicing laparoscopic procedures without formal mentoring.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".