Predictors of dropout and remission in family therapy for adolescent anorexia nervosa in a randomized clinical trial
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to explore the predictors of dropout and remission in the treatment of adolescent anorexia nervosa (AN) using family therapy. METHOD: Data derived from a randomized clinical trial comparing short and long term family therapy for adolescents with AN were used. A rotated component analysis was employed to reduce the number of variables and to address problems of collinearity and multiple testing. Dropout was defined as participating in less than 80% of the assigned therapy. Participants were classified as remitted if they obtained an ideal body weight greater than 95% and a global eating disorder Examination score within two standard deviations of community norms at the end of 12 months. RESULTS: Co-morbid psychiatric disorder and being randomized to longer treatment predicted greater dropout. The presence of co-morbid psychiatric disorder, being older, and problematic family behaviors led to lower rates of remission. A reduction of child behavioral symptoms, a decline in problematic family behaviors, and early weight gain were all within treatment changes that increased the chance of remission. CONCLUSION: Co-morbid psychiatric disorder, family behaviors, and early response to treatment are important factors when predicting dropout and remission in family therapy for adolescent 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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".