Treatment outcomes of psychotherapy for binge‐eating disorder in a randomized controlled trial: Examining the roles of childhood abuse and post‐traumatic stress disorder
Bibliographic record
Abstract
OBJECTIVE: To examine childhood abuse and post-traumatic stress disorder (PTSD) as predictors and moderators of binge-eating disorder (BED) treatment outcomes in a randomized controlled trial comparing Integrative Cognitive-Affective Therapy with cognitive-behavioural therapy administered using guided self-help. METHOD: In 112 adults with BED, childhood abuse was defined as any moderate/severe abuse as assessed by the Childhood Trauma Questionnaire, lifetime PTSD was assessed via the Structured Clinical Interview for DSM-IV, and outcomes were assessed via the Eating Disorder Examination (EDE). Covariate-adjusted regression models predicting binge-eating frequency and EDE global scores at end of treatment and 6-month follow-up were conducted. RESULTS: Lifetime PTSD predicted greater binge-eating frequency at end of treatment (B = 1.32, p = 0.009) and childhood abuse predicted greater binge-eating frequency at follow-up (B = 1.00, p = 0.001). Lifetime PTSD moderated the association between childhood abuse and binge-eating frequency at follow-up (B = 2.98, p = 0.009), such that childhood abuse predicted greater binge-eating frequency among participants with a history of PTSD (B = 3.30, p = 0.001) but not among those without a PTSD history (B = 0.31, p = 0.42). No associations with EDE global scores or interactions with treatment group were observed. CONCLUSIONS: Results suggest that a traumatic event history may hinder treatment success and that PTSD may be more influential than the trauma exposure itself.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".