Mastery in Bariatric Surgery
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of cumulative volume on all-cause morbidity and operative time. BACKGROUND: Gastric bypass is an important public health procedure, but it is difficult to master with little data about how surgeon cumulative volume affects outcomes longitudinally. METHODS: This was a longitudinal study of 29 surgeons during the first 6 years of performing bariatric surgery in a high-volume, regionalized center of excellence system. Cumulative volume was determined using date and time of the procedure. Cumulative volume was analyzed in blocks of 75 cases. The main outcome of interest was all-cause morbidity during the index admission and the secondary outcome was operative time. RESULTS: Overall, 11,684 gastric bypasses were performed by 29 surgeons at 9 centers of excellence. The overall morbidity rate was 10.1% and short-term outcomes were related significantly to cumulative volume. Perioperative risk plateaued after approximately 500 cases and was lowest for surgeons who had completed more than 600 cases (odds ratio 0.53 95% confidence interval 0.26-0.96 P = 0.04) compared to the first 75 cases. Operative time also stabilized after approximately 500 cases, with an operative time 44.7 minutes faster than surgeons in their first 75 cases (95% confidence interval 37.0-52.4 min P < 0.001). CONCLUSIONS: The present study demonstrated the clear, substantial influence of surgeon cumulative volume on improved perioperative outcomes and operative time. This finding emphasizes role of the individual surgeon in perioperative outcomes and that the true learning curve needed to master a complex surgical procedure such as gastric bypass is longer than previously thought, in this case requiring approximately 500 cases to plateau.
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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 | 0.001 |
| 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".