Personality subtyping and bulimia nervosa: psychopathological and genetic correlates
Bibliographic record
Abstract
BACKGROUND: There is empirical evidence suggesting that individuals with bulimia nervosa vary considerably in terms of psychiatric co-morbidity and personality functioning. In this study, latent profile analysis was used to attempt to identify clusters of bulimic subjects based on psychiatric co-morbidity and personality. METHOD: A total of 178 women with bulimia nervosa or a subclinical variant of bulimia nervosa completed a series of self-report inventories of co-morbid psychopathology and personality, and also provided a buccal smear sample for genetic analyses. RESULTS: Three clusters of bulimic women were identified: an affective-perfectionistic cluster, an impulsive cluster, and a low co-morbid psychopathology cluster. The clusters showed expected differences on external validation tests with both personality and eating-disorder measures. The impulsive cluster showed the highest elevations on dissocial behavior and the lowest scores on compulsivity, while the affective-perfectionistic cluster showed the highest levels of eating-disorder symptoms. The clusters did not differ on genetic variations of the serotonin transporter gene. CONCLUSIONS: This study corroborates previous findings suggesting that the bulimia nervosa diagnostic category is comprised of three classes of individuals based on co-morbid psychopathology and personality. These differences may have significant etiological and treatment 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".