The integration of minimally invasive surgery in surgical practice in a Canadian setting: results from 2 consecutive province-wide practice surveys of general surgeons over a 5-year period
Bibliographic record
Abstract
BACKGROUND: Although minimally invasive surgery (MIS) has been quickly embraced, the introduction of advanced procedures appears more complex. We assessed the evolution of MIS in the province of Quebec over a 5-year period to identify areas for improvement in the modern surgical era. METHODS: We developed, test-piloted and conducted a self-administered questionnaire among Quebec general surgeons in 2007 and 2012 to examine stated MIS practice, MIS training and barriers and facilitators to the use of MIS. RESULTS: Response rates were 51.3% (251 of 489) in 2007 and 31.3% (153 of 491) in 2012. A significant increase was observed for performance of most advanced MIS procedures, especially for colectomy for benign (66.0% v. 84.3%, p < 0,001) and malignant diseases (43.3% v. 77.8%, p < 0,001) and for rectal surgery for malignancy (21.0% v. 54.6%, p < 0.001). More surgeons practised 3 or more advanced MIS procedures in 2012 than in 2007 (82.3% v. 64.3%, p < 0,001). At multivariate analysis, the 2007 survey administration was associated with fewer surgeons practising advanced MIS (odds ratio 0.13, 95% confidence interval 0.06-0.29). In 2012, more respondents stated they gained their skills during residency (p = 0.028). CONCLUSION: From 2007 to 2012 there was a significant increase in advanced MIS procedures practised by general surgeons in Québec. This technique appears well established in current surgical practice. The growing place of MIS in residency training seems to be a paramount part of this development. Results from this study could be used as a baseline for studies focusing on ways to further improve the MIS practice.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".