Mixture Modeling to Characterize Anorexia Nervosa: Integrating Personality and Eating Disorder Psychopathology
Bibliographic record
Abstract
BACKGROUND: Efforts to examine alternative classifications (e.g., personality) of anorexia nervosa (AN) using empirical techniques are crucial to elucidate diverse symptom presentations, personality traits, and psychiatric comorbidities. AIMS: The purpose of this study was to use an empirical approach (mixture modeling) to test an alternative classification of AN as categorical, dimensional, or hybrid categorical–dimensional construct based on the co-occurrence of personality psychopathology and eating disorder clinical presentation. METHODS: Patients with AN ( N = 194) completed interviews and questionnaires at treatment admission and 3-month follow-up. Mixture modeling was used to test whether indicators best classified AN as categorical, dimensional, or hybrid. RESULTS: A four-latent class, one-latent dimension mixture model that was variant across groups provided the best fit to the data. Results suggest that all classes were characterized by low self-esteem and self-harming and suicidality tendencies. Individuals assigned to Latent Class 2 (LC2; n = 21) had a greater tendency toward being impulsive and easily angered and having difficulties controlling anger compared with those in LC1 ( n = 84) and LC3 ( n = 66). Moreover, individuals assigned to LC1 and LC3 were more likely to have a poor outcome from intensive treatment compared with those in LC4 ( n = 21). Findings indicate that the dimensional aspect within each class measured frequency of specific eating disorder behaviors but did not predict treatment outcomes. CONCLUSIONS: These results emphasize the complexity of AN and the importance of considering how facets of clinical presentation beyond eating disorder behaviors may have different treatment and prognostic implications.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".