Effect of Glucagon-like Peptide-1 Receptor Agonists (GLP-1RA) on Weight Loss Following Bariatric Treatment
Bibliographic record
Abstract
CONTEXT: There has been growing recognition of the need for considering weight-loss strategies following metabolic bariatric surgery (MBS) to limit the magnitude of potential weight regain. The use of glucagon-like peptide-1 receptor agonists (GLP-1RAs) in this setting remains uncertain. OBJECTIVE: We conducted a systematic review and meta-analysis to evaluate the effect of GLP-1RAs on weight changes in patients who previously underwent MBS. METHODS: We examined the effect of GLP-1RAs on weight changes by calculating pooled estimates (random-effects model) of the absolute differences in body weight (kg) compared to baseline for observational studies and compared to a control group for randomized controlled trials (RCTs). A total of 17 studies (1164 participants) met our inclusion criteria. Pooling the data from the 14 observational studies evaluating the effect of GLP-1RAs post bariatric treatment demonstrated a reduction of 7.83 kg compared to pre treatment (before the use of GLP-1RA) (weight-7.83 kg [95% CI, -9.27 to -6.38]). With respect to tolerability, 23% (95% CI, 10%-36%) of participants reported any adverse event but only 7% discontinued treatment. Data from RCTs showed that the use of GLP-1RAs induced weight reduction of 4.36 kg (95% CI, -0.42 to -8.30) compared to placebo with a similar safety profile. CONCLUSION: Our findings suggest that the use of liraglutide and semaglutide in patients who previously underwent MBS can promote significant weight reduction with an acceptable safety profile.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".