The Use of Silicone Simulators for Bile Duct Anastomosis Education in Medical Conferences for the Purpose of Improving the Canadian Medical Education Directives for Specialists (CanMEDS) Competencies
Bibliographic record
Abstract
This technical report explores the potential of including silicone bile duct simulators for the purpose of completing a bile duct anastomosis (BDA) in medical conferences. The purpose is to target the need for exposure to more surgical skills and to contribute to the Canadian Medical Education Directives for Specialists (CanMEDS) requirements, as per the Royal College of Physicians and Surgeons of Canada. Data collection was completed at the 2023 Canadian Conference for the Advancement of Surgical Education (C-CASE) in Montreal, Canada. For several years, the quality improvement feedback received at the end of these conferences suggested a few areas of improvement, one of which was related to the concept of return on investment (ROI). The participants spend a considerable amount of funds to travel to the conferences but feel that the only measurable gains are at a research capacity and thus only relate to two CanMEDS competencies. By leveraging C-CASE, the aim is to enhance students' educational experience during events they already intend to attend. Initially, students participated in a five-part simulation workshop and engaged in a think-aloud protocol (TAO). From there, nine participants were recruited for a focus group to further understand the perceived educational value and feedback on both the simulators and the conference structure.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".