A systematic review of enhanced cognitive behavioral therapy (CBT‐E) for eating disorders
Bibliographic record
Abstract
OBJECTIVE: To review the literature examining the efficacy and effectiveness of enhanced cognitive behavioral therapy (CBT-E) for adults and older adolescents with eating disorders. METHOD: A systematic search of the literature (using PsycINFO and PubMed) was conducted in order to identify relevant publications (randomized controlled trials [RCTs] and uncontrolled trials) up to June 2019. Effect sizes were reported for outcomes including treatment attrition and remission rates, eating disorder behaviors, body mass index (BMI), and core eating disorder psychopathology. The Downs and Black checklist was used to assess the quality of included studies. RESULTS: Twenty studies (10 RCTs and 10 uncontrolled trials) met criteria for inclusion. Support was found for the efficacy and effectiveness of CBT-E for the full spectrum of eating disorders, with respect to reducing eating disorder behaviors and core psychopathology. BMI also increased, with large effects, for individuals with AN. However, the majority of the randomized trials included in this review did not demonstrate superiority of CBT-E over comparison treatments, particularly in the longer-term. Furthermore, rates of attrition and remission for CBT-E among individuals without AN did not appear to differ from rates for CBT-BN. DISCUSSION: There is evidence to support CBT-E as an efficacious and effective treatment for adults and older adolescents with a range of eating disorder diagnoses. Future research would benefit from directly comparing CBT-E to CBT-BN, expanding measured outcomes to include driven exercise and subjective binge eating, increasing consistency in the definition and measurement of outcomes, and exploring factors associated with treatment retention.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".