Comparative efficacy and acceptability of interventions for universal, selective and indicated prevention of eating disorders: study protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
Abstract Background Eating disorders (EDs) are severe psychiatric conditions, with prevalence rates ranging from 5.5 to 17.9% in women and 0.6 to 2.4% in men. EDs carry a high risk of chronicity and mortality, highlighting the need for effective prevention strategies. Primary prevention can target the entire population (universal), high-risk groups (selective), or individuals with early signs (indicated). Despite substantial research, prior reviews often show limitations, such as single-author data extraction, lack of quality assessment, reliance on endpoint data, exclusion of obesity prevention programs, or outdated findings. No review has yet evaluated the comparative effectiveness of multiple interventions across risk groups. This article outlines a systematic review and network meta-analysis (NMA) protocol to assess the comparative effectiveness of various ED preventive interventions across different prevention types and populations. Methods Eligible studies will include (cluster) randomized controlled trials (RCTs) involving children, adolescents, and adults across a range of settings. Databases to be searched include MEDLINE, Embase, PsycINFO, and CENTRAL. All prevention types (universal, selective, indicated) will be included. Interventions will encompass psychological, educational, physical, and nutritional approaches aimed at preventing EDs, disordered eating, or negative body image and/or reducing risk factors. Coprimary outcomes will be ED diagnostic symptoms, overall ED pathology, ED onset, and intervention all-cause discontinuation (acceptability). A frequentist NMA framework will be used for data synthesis, with sensitivity and subgroup analyses to identify effect modifiers. Discussion This first NMA on ED prevention aims to provide valuable insights for clinicians, researchers, policymakers and the public by identifying the most effective interventions and highlighting research gaps. The findings will inform intervention selection for specific populations and guide future prevention strategies to reduce the burden of EDs on affected individuals, their communities, and wider society. Clinical trial registration number CRD42024498102.
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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 | 0.056 | 0.088 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.032 | 0.042 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.003 |
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".