Do Behavioral Interventions Delivered Before Bariatric Surgery Impact Weight Loss in Adults? A Systematic Scoping Review
Bibliographic record
Abstract
Background: The health benefits of interventions occurring before surgery have not been addressed as a whole. The objective of this review was to investigate the impact of presurgical behavioral interventions on weight-related measures among adults. Methods: The review utilized the guidelines established by the Preferred Reporting Items for Systematic Review and Meta-Analyses statement. Thereafter, scoping review methods were used to report and summarize what is known about the specific topic to date. Relevant articles were identified by databases up to July 6, 2015. Results: A total of eight studies were identified for inclusion in the review. Across five of the studies, patients in treatment groups demonstrated greater weight loss improvement than control/comparator groups at follow-up. Generally, the impact of presurgical behavioral interventions facilitated improvements in weight-related measures and demonstrated the utility of behavior modification treatment within their programs. Conclusions: The importance of implementing routine behavioral treatment into bariatric surgery protocol, whether before and/or after surgery, may prevent patients from experiencing weight regain. Overall, this systematic scoping review provides an evidence-based overview of behavior modification therapies offered before weight loss surgery, which promotes weight reduction and may potentially better prepare individuals for long-term obesity management.
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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 | 0.089 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".