The feasibility of introducing advanced minimally invasive surgery into surgical practice.
Bibliographic record
Abstract
BACKGROUND: This study investigates the feasibility of performing advanced minimally invasive surgery (MIS) in a nonspecialized practice environment. METHODS: We conducted a cross-sectional survey of all community general surgeons currently practising in Ontario. RESULTS: Few community surgeons perform a high volume (> 10 procedures per yr) of advanced MIS. Most (70%) believe it is important to acquire additional skills in advanced MIS. The most appropriate methods for learning advanced MIS are believed to be expert mentoring (79.7%), courses (77.2%) and a colleague mentor (63.9%). A total of 57.6% of respondents have attended a course in MIS while in practice, and most have access to a reasonable variety of instrumentation. Respondents believe that 57.6% of assistants, 54.8% of nurses and 43.4% of anaesthetists are relatively inexperienced with advanced MIS. Barriers to establishing advanced MIS include limited operating room access (50%), resources or equipment (45.2%) and limited expert mentoring (43.6%). Surgeons with less than 10 years of practice found lack of trained nursing staff (7.9% v. 4.2%, p = 0.01) and experienced assistants (12% v. 6.2%, p = 0.008) to be more important barriers than did those with over 10 years of practice, respectively. CONCLUSION: Most general surgeons working in Ontario are self-taught with respect to MIS skills, and few perform a high volume of advanced MIS. Only one-half of all respondents have access to skilled MIS operating room nurses, surgical assistants or anesthesiology. Despite this, general surgeons perceive the greatest barriers to introducing advanced MIS procedures to be limited access to operating rooms, resources or equipment and limited mentoring. This study has shown that the role of the surgical team in advanced MIS may be underestimated by many general surgeons. These data have important implications in training general surgeons and in incorporating additional advanced MIS procedures into the armamentarium of general surgeons.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".