Delivery formats of cognitive behavior therapy in adults with eating disorders: a network meta-analysis
Bibliographic record
Abstract
Although CBT has been found to be effective in the treatment of eating disorders, it is not clear if there are differences between treatment formats. We conducted a network meta-analysis (NMA) of randomized trials of broadly defined CBT comparing individual, group, guided self-help (GSH) and unguided self-help (USH) with each other or with a control condition. The NMA used a frequentist graph-theoretical approach and included 36 trials (53 comparisons; 3,136 participants). Only one trial was aimed at anorexia nervosa. All formats resulted in large, significant effects when compared to waitlists, with no significant difference between formats (group: g = 1.08, 95% CI: 0.84; 1.31; GSH: g = 0.94, 95% CI: 0.75; 1.13; individual: g = 1.06, 95% CI: 0.77; 1.36; USH: g = 0.62, 95% CI: 0.30; 0.93). No significant difference was found between any format and care-as-usual. Analyses limited to binge eating disorder supported the effects of individual, group and GSH formats, with no significant differences between them. Few trials with low risk of bias were available. CBT for eating disorders can probably be delivered effectively in any format, without significant differences between the formats. These results should be considered with caution because of the non-significant differences when compared to care-as-usual and the considerable risk of bias.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 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".