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
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: 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.
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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.082 | 0.021 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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