Simulation-based training and learning curves in laparoscopic Roux-en-Y gastric bypass
Bibliographic record
Abstract
BACKGROUND: Ex vivo simulation-based technical skills training has been shown to improve operating room performance and shorten learning curves for basic laparoscopic procedures. The application of such training for laparoscopic Roux-en-Y gastric bypass (LRYGBP) has not been reviewed. METHODS: Relevant studies were identified by one author from a search of MEDLINE and Embase databases from 1 January 1994 to 30 November 2010. Studies examining the learning curves and ex vivo training methods for LRYGBP were included; all other types of bariatric operations were excluded. A manual search of the references was also performed to identify additional potentially relevant papers. RESULTS: Twelve studies (5 prospective and 7 retrospective case series) were selected for review. The learning curve for LRYGBP was reported to be 50-100 procedures. Bench-top laparoscopic jejunojejunostomy, anaesthetized animals and Thiel human cadavers made up the bulk of the reported models for ex vivo training. Most studies were of relatively poor quality. An evidence-based ex vivo training curriculum for LRYGBP is currently lacking. CONCLUSION: Better quality studies are needed to define the learning curve for LRYGBP. Future studies should focus on the design and validation of training models, and a comprehensive curriculum for training and assessment of cognitive, technical and non-technical components of competency for laparoscopic bariatric surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".