Comparison of laparoscopic Roux-en-Y gastric bypass with laparoscopic sleeve gastrectomy for morbid obesity or type 2 diabetes mellitus: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Laparoscopic Roux-en-Y gastric bypass (LRYGB) is one of the most widely used bariatric procedures, and laparoscopic sleeve gastrectomy (LSG) as a single-stage procedure for treating morbid obesity is becoming more popular. We compared both techniques to evaluate their efficacy in treating morbid obesity or type 2 diabetes mellitus (T2DM). METHODS: We searched the Cochrane Controlled Trials Register databases, Medline, Embase, ISI databases and the Chinese Biomedical Literature Database to identify randomized controlled trials (RCTs) of LRYGB and LSG for morbid obesity or T2DM published in any language. Statistical analyses were carried out using RevMan software. RESULTS: Five worldwide RCTs with 196 patients in the LRYGB group and 200 in the LSG group were included in our analysis. Compared with patients who had LSG, those who had LRYGB had a higher remission rate of T2MD, lost more weight and had lower low-density lipoprotein, triglycerides, homeostasis model assessment index and insulin levels. There was no difference in the reoperation rate between the groups. However, patients treated with LRYGB had a higher incidence of complication than those treated with LSG. CONCLUSION: Our meta-analysis demonstrates that LRYGB is more effective than LSG for the surgical treatment of T2DM and control of metabolic syndrome. However, LSG is safer and has a reduced rate of complications. Further high-quality RCTs with long follow-up periods are needed to provide more reliable evidence.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.047 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".