ThP3.13 - Impact of gastrojejunostomy anastomosis diameter on weight loss following laparoscopic gastric bypass: a systematic review
Bibliographic record
Abstract
Abstract Introduction Laparoscopic Roux-en-Y Gastric Bypass (RYGB) is a preeminent procedure for achieving significant weight reduction and alleviating obesity-related comorbidities. However, the influence of gastrojejunostomy (GJ) anastomosis diameter on weight loss is not well-elucidated. This review examines the effect of GJ diameter on post-RYGB weight loss outcomes. Methods A meticulous systematic search of the literature, limited to English-language publications, was executed with the assistance of an expert librarian. The search encompassed all studies available up to 3 August 2023, the date of the last search. Study quality was evaluated utilizing the Newcastle-Ottawa Scale. Due to the heterogeneity of the outcomes, a narrative synthesis approach was adopted. Results From 1,026 records screened, 6 studies met the inclusion criteria, demonstrating a range of GJ diameters and follow-up durations (1-5 years). Generally, smaller GJ diameters were associated with more significant weight loss in the short to medium term. There appears to be a threshold for the reduction in diameter; beyond this, the risk of complications such as stenosis may escalate. The studies indicated a moderate to low risk of bias and underscored the importance of accurate GJ area quantification post-operation. Conclusion This review identifies a negative correlation between smaller GJ diameters and weight loss after RYGB. Future investigations should prioritize the standardization of measurement techniques and investigate the potential of intra-operative and artificial intelligence-based methodologies for optimal GJ diameter determination, with the aim of augmenting patient outcomes in 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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".