State/trait distinctions in bulimic syndromes
Bibliographic record
Abstract
OBJECTIVE: This study compared 55 women with active bulimic symptoms, 18 in remission from a bulimic eating disorder, and 31 who showed no evidence of a past or present eating disorder, on selected personality and psychiatric features. METHOD: Discriminant function analyses were used to isolate dimensions that differentiated active patients from patients in remission, and controls (i.e., that would logically constitute "state"-related disturbances), and then dimensions that differentiated clinical cases (whether active or in remission) from non-eating-disordered controls (i.e., that might reflect stable trait pathology associated with bulimic syndromes, whether active or not). RESULTS: Measures of depression, suicidality, and anxiety loaded significantly on the first function (differentiating active bingers from all other cases), whereas narcissism differentiated both clinical groups from non-eating-disordered controls. DISCUSSION: In light of theoretical and empirical evidence stressing the etiological role of narcissistic disturbances in bulimic syndromes, we interpret our findings as suggesting that narcissim may be a common trait characteristic (persisting even after remission of bulimic symptoms) in those who develop bulimic eating syndromes. Alternatively, depression, suicidality, and anxiety appear to be state-dependent features that resolve in many cases, along with remission of bulimic symptoms. We discuss various clinical and theoretical implications of our findings.
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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 | 0.000 |
| 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) | 0.001 | 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".