Common surgical training program: standardization of learning quality
Bibliographic record
Abstract
INTRODUCTION: The various surgical specialties in our center have used the simulation and experimental surgery resources available for their training tasks in minimally invasive surgery (MIS) in an individualized manner. With this learning model, a great dispersion of effort and expense was observed, so it was decided to create a unified program based on the following: shared learning, synergy among specialties, moderation of the economic cost, and rational use of the facilities. OBJECTIVE: To describe and assess our consensually designed training program in order to consolidate a shared learning strategy that will enable our residents to acquire and perfect surgical skills in MIS. MATERIALS AND METHODS: The program consists of various increasingly complex phases implemented on a continuous basis throughout the period of specialized training in the virtual laboratory and experimental operating room. The assessment methods were based on quantifiable criteria: percentage of efficiency and completion time of the "McGill Inanimate System for Training and Evaluation of Laparoscopic Skills" (MISTELS) exercises at the beginning and end of the program. An economic study was also conducted. RESULTS: 20 residents have completed the program. Mean times show a significant reduction in each of the exercises. The efficiency percentages at the end of the program were higher than at the beginning (p < 0.001). The cost of the program represented a saving of 67.89%. CONCLUSION: The new MIS training program improved the quality of learning in a safe environment, establishing common criteria among the different specialties and an improved use of resources.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".