1433-P: Comparative Weight Loss with GLP-1 Receptor Agonists vs. Bariatric Surgery in Obesity: A Systematic Review and Pair-Wise Meta-analysis
Bibliographic record
Abstract
Background: One in three adults suffers from obesity.1 Glucagon-like peptide-1 (GLP-1) receptor agonists have been associated with 15-20% weight loss, a range previously only achieved with bariatric surgery.2,3,4 This systematic review and meta-analysis compares weight loss between GLP-1 receptor agonists and bariatric surgery. Methods: MEDLINE, Medline-in-Process, Medline EPUBS Ahead of Print, EMBASE Classic + EMBASE (OvidSP) ; and Cochrane (Wiley) databases were searched from inception to April 21, 2021. We included randomized controlled trials (RCTs) and observational studies. Two independent reviewers extracted data, reported risk of bias, and graded certainty of evidence. Risk of bias was reported using Risk of Bias 2 (RoB 2) tool for RCTs and Risk of Bias in Non-randomized Studies- of Interventions (ROBINS-I) tool for observational studies. Data were pooled separately for RCTs and observational studies using random effects models. Change in weight, body mass index (BMI) , and glycated hemoglobin (HbA1C) were summarized. Results: Seven included studies encompassed 11patients. Among RCTs, mean difference in weight between bariatric surgery and GLP-1 receptor agonist was −22.68 (95% CI: −31.41, −13.96) , mean difference in BMI was −8.18 (95% CI: −11.59, −4.77) , and mean difference in HbA1C was −1.28 (95% CI: −1.94, −0.61) . Among observational studies, mean difference in weight was −16.56 (95% CI: −36.53, 3.42) and mean change in BMI was −10.60 (95% CI:−17.22, −3.98) . These effects all favoured bariatric surgery. Conclusion: In adults with obesity, bariatric surgery still confers highest reductions in weight and BMI compared to GLP-1 receptor agonists, but similar effects in glycemic control. Word count: 248 Disclosure P. Palcu: None. Funding Clinician Investigator Program, University of Toronto
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.052 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".