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
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data extraction, lack of quality assessment, reliance on endpoint data, exclusion of obesity prevention programs, or outdated findings.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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".