An evidence-based laparoscopic simulation curriculum shortens the clinical learning curve and reduces surgical adverse events
Bibliographic record
Abstract
BACKGROUND: Surgical simulation is becoming increasingly important in surgical education. However, the method of simulation to be incorporated into a surgical curriculum is unclear. We compared the effectiveness of a proficiency-based preclinical simulation training in laparoscopy with conventional surgical training and conventional surgical training interspersed with standard simulation sessions. MATERIALS AND METHODS: In this prospective single-blinded trial, 30 final-year medical students were randomized into three groups, which differed in the way they were exposed to laparoscopic simulation training. The control group received only clinical training during residency, whereas the interval group received clinical training in combination with simulation training. The Center for Surgical Technologies Preclinical Training Program (CST PTP) group received a proficiency-based preclinical simulation course during the final year of medical school but was not exposed to any extra simulation training during surgical residency. After 6 months of surgical residency, the influence on the learning curve while performing five consecutive human laparoscopic cholecystectomies was evaluated with motion tracking, time, Global Operative Assessment of Laparoscopic Skills, and number of adverse events (perforation of gall bladder, bleeding, and damage to liver tissue). RESULTS: The odds of adverse events were 4.5 (95% confidence interval 1.3-15.3) and 3.9 (95% confidence interval 1.5-9.7) times lower for the CST PTP group compared with the control and interval groups. For raw time, corrected time, movements, path length, and Global Operative Assessment of Laparoscopic Skills, the CST PTP trainees nearly always started at a better level and were never outperformed by the other trainees. CONCLUSION: Proficiency-based preclinical training has a positive impact on the learning curve of a laparoscopic cholecystectomy and diminishes adverse events.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".