Relapse in anorexia nervosa: a survival analysis
Bibliographic record
Abstract
BACKGROUND: Knowledge about factors that predict relapse in anorexia nervosa (AN) is needed for the development of effective relapse prevention treatments and may also advance understanding of the psychopathology of AN. The aim of the present study was to examine the rate, timing and prediction of relapse in AN following weight restoration in a specialized in-patient treatment programme. METHOD: Fifty-one consecutive first-admission AN patients who were weight-restored following in-patient treatment participated in the study. Follow-up assessments were conducted a median of 15 months post-discharge. Relapse of AN was defined as a body mass index <17.5 for 3 consecutive months. Data were analysed using Kaplan-Meier survival analysis and Cox regression. RESULTS: The overall rate of relapse was 35% and the mean survival time was 18 months. The highest risk period was from 6 to 17 months after discharge. Several significant predictors of relapse were identified: a history of suicide attempt; previous specialized treatment for an eating disorder; severity of obsessive-compulsive symptoms at presentation; excessive exercise immediately after discharge; and residual concern about shape and weight at discharge. CONCLUSIONS: There continues to be a significant risk of relapse among AN patients who remain well for the first year post-discharge. Several variables were shown to be associated with an elevated risk of relapse. These findings have implications for the development of initial treatments and relapse prevention strategies for AN.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".