Psychotherapy for eating disorders: A meta-analysis of direct comparisons
Bibliographic record
Abstract
Objective: We conducted a meta-analysis of randomized controlled trials (RCTs) of bona fide psychotherapy for adults with eating disorders (EDs). Method: Thirty-five RCTs with 54 direct comparisons were included. The majority of RCTs included participants with bulimia nervosa and/or binge-ED, while only two RCTs included participants with anorexia nervosa, and three RCTs included participants with an ED not otherwise specified. Results: There was a clear advantage of bona fide psychotherapy over wait-list controls. Bona fide psychotherapy was superior to non-bona fide treatment; however, the majority of results were not stable. There were no significant differences between bona fide cognitive–behavioral therapy (CBT) and bona fide non-CBT, with the exception of bona fide CBT resulting in greater reductions in ED psychopathology assessed by the ED Examination, which primarily assesses maintenance factors according to the CBT model. Conclusions: Generally, the results indicate that any bona fide psychotherapy will be equally effective. While the number of trials remains modest, we hope that as more research becomes available, treatment guidelines can be updated, and more evidence-based treatment options will be available for treating EDs.
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 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 | 0.059 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".