The Evaluation of Learning Curves in Novice Laparoscopists: Incorporating Direct Visualization into the Simulation Training Program
Bibliographic record
Abstract
Technological developments have led to resurgent interest in stereoscopic visualization for laparoscopy. Evidence suggests that trainees achieve proficiency on the McGill Inanimate System for Training and Evaluation of Laparoscopic Skills (MISTELS) in less total training time when using stereo visualization. Data from trainees using the Fundamentals of Laparoscopic Surgery (FLS) curriculum to train the MISTELS tasks indicate that the peg transfer task requires the greatest training time to reach proficiency. This task challenges basic hand‐eye coordination that is critical to performing more complex tasks. As monoscopic visualization remains the standard of care, our goal remains to train proficiency under monoscopic visualization. However, we propose incorporating stereo‐direct visualization (SDV) using open FLS box trainers, into the curriculum to accelerate learning. Novice surgery residents will train with the peg transfer task. Half will train to proficiency (< 54 sec) under standard monoscopic video visualization. The SDV group will train to proficiency, then switch to traditional monoscopic video visualization and continue to train until proficiency is reached. We hypothesize that SDV training will facilitate the perceptual switch to reach proficiency in less total time. Results from this study may be employed to further guide the development of laparoscopic training curricula.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".