Psychiatric Comorbidity and Eating Disorder Inventory (EDI) Profiles in Eating Disorder Patients
Bibliographic record
Abstract
OBJECTIVE: This study examines potential overlaps between psychiatric comorbidity (Axis I and II) and scores on the subscales of the Eating Disorder Inventory (EDI) in women with eating disorders (EDs). METHOD: In a sample of 248 women (72 with anorexia nervosa, 140 with bulimia nervosa, and 36 with eating disorders not otherwise specified), we determined psychiatric comorbidity using the Structured Clinical Interview for DSM-IV. Behavioural and psychological characteristics of EDs were quantified with the EDI. RESULTS: Psychiatric comorbidity was high in both axes (74% for Axis I and 68% for Axis II). While most EDI subscales pertaining to psychological traits showed significant associations with Axis I and II disorders, the subscales concerning eating and perception of weight and shape were much less associated with psychiatric comorbidity. Affective and anxiety disorders, as well as personality disorders of clusters A and C, showed a similar pattern with links to most psychological subscales. The profile for substance-related disorders was different, showing associations with the Ineffectiveness and Interoceptive Awareness scales. Personality disorders of cluster B were related only to the Bulimia subscale and not to any of the psychological subscales. CONCLUSIONS: The EDI appears to primarily reflect Axis I and II disorders related to affective and anxiety problems. Clinicians and researchers employing the EDI should be aware that it is not sensitive for all forms of comorbidity prevalent in ED patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".