The adoption of laparoscopic colorectal surgery: a national survey of general surgeons.
Bibliographic record
Abstract
BACKGROUND: Laparoscopic surgery may become the standard of care for the treatment of colorectal disease. Little is known regarding North American patterns of practice or the limiting factors and strategies for adoption among surgeons. METHODS: We sent a 28-item questionnaire to all general surgeon members of the Royal College of Physicians and Surgeons of Canada. We derived descriptive and correlative information using chi(2), Wilcoxon rank sum and Student t tests and multivariate logistic regression. RESULTS: The return rate was 55% (694/1266). A total of 67% (462/694; 95% confidence interval 63%-70%) of respondents perform colorectal surgery. Of these, 54% perform laparoscopic colorectal surgery. Multivariate logistic regression identified 5 factors related to performing laparoscopic colorectal surgery: fewer years in practice (p < 0.001), male sex (p = 0.015), practising in the province of Quebec (p = 0.005), university-hospital affiliation (p = 0.034) and minimally invasive surgery fellowship training (p = 0.023). Lack of adequate operating time and formal training were the main reasons cited by surgeons not offering laparoscopic colon resections. Most surgeons (67%) felt that site visits from a minimally invasive surgeon would represent the most effective training method for acquiring advanced laparoscopic skills. CONCLUSION: About half of Canadian general surgeons offer laparoscopic colorectal resections. Recent graduation, male sex, practice location, university-hospital affiliation and minimally invasive surgery training are significant predictors for offering a laparoscopic approach. Lack of operative time and formal training are the main barriers to adoption of the technique. Site visits by trained laparoscopic surgeons is the preferred method of acquiring advanced skills.
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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 | 0.003 |
| 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) | 0.001 | 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".