Timing and prediction of relapse in a transdiagnostic eating disorder sample
Bibliographic record
Abstract
OBJECTIVE: To identify factors that predict relapse in eating disorders to direct the development of effective relapse prevention interventions. METHOD: Fifty-eight participants who had partially remitted from their eating disorder after intensive treatment were prospectively followed for up to 24 months. A transdiagnostic sample was included based on current recommendations. RESULTS: The 12-month survival rate was 0.59, indicating that 41% of the sample had relapsed at this time, and four factors emerged as significant predictors of relapse. These factors included more severe pretreatment caloric restriction, higher residual symptoms at discharge, slower response to treatment, and higher weight-related self-evaluation. CONCLUSION: Clinical recommendations based on these data include encouraging clients to adopt the recommended behavioral changes immediately at the beginning of treatment, and to make complete symptom control a priority. In addition, addressing weight-related self-evaluation and teaching clients to detach from this schema that connects weight/shape with self-esteem may be an effective and feasible step toward relapse prevention.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".