Behavioral weight management interventions in metabolic and bariatric surgery: A systematic review and meta‐analysis investigating optimal delivery timing
Bibliographic record
Abstract
Summary Metabolic and bariatric surgery (MBS) yields unprecedented clinical outcomes, though variability is high in weight change and health benefits. Behavioral weight management (BWM) interventions may optimize MBS outcomes. However, there is a lack of an evidence base to inform their use in practice, particularly regarding optimal delivery timing. This paper evaluated the efficacy of BWM conducted pre‐ versus post‐ versus pre‐ and post‐MBS. The review followed the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses statement and included pre‐ and/or post‐operative BWM interventions in adults reporting anthropometric and/or body composition data. Thirty‐six studies (2,919 participants) were included. Post‐operative BWM yielded greater decreases in weight (standardized mean difference [SMD] = −0.41; 95% confidence interval [CI]: −0.766 to −0.049, p < 0.05; I 2 = 93.5%) and body mass index (SMD = −0.60; 95% CI: −0.913 to −0.289, p < 0.001; I 2 = 87.8%) relative to comparators. There was no effect of BWM delivered pre ‐ or joint pre‐ and post‐operatively. The risk of selection and performance bias was generally high. Delivering BWM after MBS appears to confer the most benefits on weight, though there was high variability in study characteristics and risk of bias across trials. This provides insight into the type of support that should be considered post‐operatively.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".