The impact of a comprehensive course in advanced minimal access surgery on surgeon practice.
Bibliographic record
Abstract
INTRODUCTION: Practising surgeons need an effective means for learning new skills and procedures in advanced minimal access surgery (MASA). Currently, available educational methods include traditional continuing medical education symposia (1-day courses), instructional videos, mentoring, or comprehensive courses that combine lectures, skills laboratories and live surgery. The impact of comprehensive courses in advanced MASA on surgeons' knowledge, skills and practice has not been clearly established. METHODS: We completed a survey of all physicians who attended comprehensive courses in advanced gastrointestinal MASA held at the Centre for Minimal Access Surgery (CMAS) in Hamilton, Ont. RESULTS: Of 158 course attendees, we received 65 responses (response rate 41%). Fifty-six men and 9 women responded, with a mean age of 44.9 years and a mean practice duration of 12.3 years. Eighty-seven percent of respondents were community-based surgeons. As a result of attending CMAS courses, respondents felt they experienced a substantial improvement in the knowledge and skills required to complete MASA. After a comprehensive course at CMAS, most respondents reported that they had introduced MASA procedures into their practice. The mean overall impact of a course on a surgeon's practice (with respect to patient referrals, procedural armamentarium and personal satisfaction) was rated by respondents at 3.92 (standard deviation [SD] 0.71; Likert scale 1-5, 1=negative, 5=positive). CONCLUSIONS: A comprehensive course in advanced MASA has a positive impact on attendees' knowledge and skills. Ultimately, surgeons attending MASA courses will begin to introduce new MASA procedures into surgical practice. These courses have a distinct role in the teaching of MASA to surgeons in 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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".