Advancing excellence: a national peer-coaching program for advanced laparoscopic HPB techniques
Bibliographic record
Abstract
BACKGROUND: Surgical coaching is valuable for disseminating knowledge, refining skills, and fostering continuous professional development for surgeons in practice. This work aims to implement a national coaching program for Canadian HPB surgeons, emphasizing advanced laparoscopic techniques, and to assess subsequent adoption. Secondary objectives include evaluating surgeon perceptions, barriers, and experiences. METHODS: Mid-to-late career HPB surgeons across Canada joined a peer surgical coaching program for advanced laparoscopic skills. The program included didactic sessions followed by practical coaching with case observation, simulation labs, and real-time coaching in the operating room. One lead surgeon from each center was invited to participate in the exit interview. RESULTS: Eight centers across four provinces completed the program, and one lead surgeon from each site was interviewed. Surgeons reported a 34.9 % increase in self-perceived comfort levels in laparoscopic HPB surgeries, with a 24.2 % and 56.7 % increase in laparoscopic liver and pancreas resections, respectively. Participants acknowledged challenges in implementing surgical coaching, citing barriers related to surgeon and societal factors. Overcoming these challenges required mutual respect, openness to learning, and building sustained change through team collaboration and long-term coach relationships. DISCUSSION: This work demonstrated the practicality of a nationwide coaching program and its capacity to effect substantial, long-term change in clinical 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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".