Predicting the outcome of eating disorders using structural equation modeling
Bibliographic record
Abstract
OBJECTIVE: There is a need for models that predict accurately the course of mental disorders. METHOD: Eating-disordered female inpatients were assessed longitudinally at the beginning of treatment (t1), at the end of treatment (t2), at 2 or 3-year follow-up (t3), and at 6-year follow-up (t4). The sample consisted of 196 women with bulimia nervosa (BN) purging type, 103 women with anorexia nervosa (AN), and 68 women with binge eating disorder (BED; N=367). Confirmatory factor analysis and path analysis were used to predict the women's status at 6-year follow-up. RESULTS: The results for BN and BED show that the specific eating disorder pathology was influenced mainly by specific eating disorder pathology at earlier time points and not by non-eating-specific (general) psychopathology. Similarly, general psychopathology was influenced mainly by general psychopathology at earlier time points. For AN patients, both categories of psychopathology (eating specific and general) were relevant for the 6-year outcome. The potential impact of 14 factors on the level of pathology was estimated (a) at baseline (at the beginning of treatment), (b) during the course of illness (baseline controlled), and (c) on the 6-year outcome of eating disorders (baseline and course controlled). Although there were many correlations between potential factors and baseline pathology, there was only a limited number of significant correlations with the 6-year outcome. This effect was mediated largely by the level of general psychopathology. DISCUSSION: The models for outcome prediction based on structural equation modeling techniques were very similar for BN and BED. For both BN and BED, there were almost entirely separate predictions for the specific eating disorder on the one hand and non-eating-related (general) psychopathology on the other hand. This was true to a lesser degree for AN. CONCLUSIONS: The use of refined path analytic methods in follow-up studies on larger general populations will be helpful to increase our understanding of the course of illness of psychiatric disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".