Nonsurgical weight loss interventions: A systematic review of systematic reviews and meta‐analyses
Bibliographic record
Abstract
Access to bariatric surgery to treat obesity is limited and has long wait times. Many adults are seeking nonsurgical weight loss support. Our study objective was to conduct a systematic review of nonsurgical weight loss interventions. PubMed, EMBASE, CINAHL, PsycInfo, and the CochraneLibrary were searched. Inclusion criteria were adults 18 + with a BMI > 25 enrolled in minimum a 3-month nonsurgical weight loss intervention. Studies were independently extracted and assessed for quality using A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR2). Pooled analyses were extracted, graded for evidence quality, and summarized. A total of 1065 studies were assessed for eligibility; 815 screened and 236 full-texts assessed. Sixty-four meta-analyses met eligibility criteria: 1180 RCTs with 184,605 study participants. Studies were categorized as diets (n = 13), combination therapies (n = 10), alternative (n = 16), technology (n = 10), behavioral (n = 5), physical activity (n = 6), and pharmacotherapy (n = 3). In 80% of studies, significant weight losses were reported ranging from 0.34-8.73 k in favor of the intervention. The most effective nonsurgical weight loss interventions were diets, either low-carbohydrate or low-fat diets, followed for 6 months; combination therapy including meal replacements plus enhanced support; and pharmacotherapy followed for 12 months. Although significant weight losses were reported for other types of interventions such as physical activity and technology, the majority of studies reported weight losses less than 2 kgs.
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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 | 0.026 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.031 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".