Simulation-based training and learning curves in laparoscopic Roux-en-Y gastric bypass
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it