Understanding the Association of Impulsivity, Obsessions, and Compulsions with Binge Eating and Purging Behaviours in Anorexia Nervosa
Bibliographic record
Abstract
OBJECTIVE: To further refine our understanding of impulsivity, obsessions, and compulsions in anorexia nervosa (AN) by isolating which behaviours--binge eating, purging, or both--are associated with these features. METHODS: We conducted regression analyses with binge eating, purging, and the interaction of binge eating with purging as individual predictors of scores for impulsivity, obsessions, and compulsions in two samples of women with AN (n = 1373). RESULTS: Purging, but not binge eating, was associated with higher scores on impulsivity, obsessions, and compulsions. Purging was also associated with worst eating rituals and with worst eating preoccupations. CONCLUSION: Our results suggest that purging, compared with binge eating, may be a stronger correlate of impulsivity, obsessions, and compulsions in AN.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".