The Steinberg-Bernstein Centre for Minimally Invasive Surgery at McGill University
Bibliographic record
Abstract
Surgical skills and simulation centers have been developed in recent years to meet the educational needs of practicing surgeons, residents, and students. The rapid pace of innovation in surgical procedures and technology, as well as the overarching desire to enhance patient safety, have driven the development of simulation technology and new paradigms for surgical education. McGill University has implemented an innovative approach to surgical education in the field of minimally invasive surgery. The goal is to measure surgical performance in the operating room using practical, reliable, and valid metrics, which allow the educational needs of the learner to be established and enable feedback and performance to be tracked over time. The GOALS system and the MISTELS program have been developed to measure operative performance and minimally invasive surgical technical skills in the inanimate skills lab, respectively. The MISTELS laparoscopic simulation-training program has been incorporated as the manual skills education and evaluation component of the Fundamentals of Laparoscopic Surgery program distributed by the Society of American Gastrointestinal and Endoscopic Surgeons (SAGES) and the American College of Surgeons.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.023 |
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".