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Record W4409905186 · doi:10.1080/16506073.2025.2495950

Delivery formats of cognitive behavior therapy in adults with eating disorders: a network meta-analysis

2025· article· en· W4409905186 on OpenAlexaff
Pim Cuijpers, Mathias Harrer, Clara Miguel, Tara Donker, Aaron Keshen, Eirini Karyotaki, Jake Linardon

Bibliographic record

VenueCognitive Behaviour Therapy · 2025
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsDalhousie University
FundersNational Health and Medical Research CouncilMedical Research Council
KeywordsEating disordersPsychologyPsychotherapistCognitionCognitive behaviour therapyClinical psychologyMeta-analysisCognitive behavioral therapyMedicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.051
GPT teacher head0.344
Teacher spread0.293 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes1
Has abstractyes

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