Is history of abuse predictive of eating disorders with binge-eating episodes through an effect mediated by impulsivity? A French longitudinal study
Bibliographic record
Abstract
BACKGROUND: In recent years, many studies have explored the associations among impulsivity, history of abuse, the emergence of eating disorders with episodes of binge eating (EDBE) and their severity. Nevertheless, factors associated with successful clinical outcomes of EDBE are still unknown. Our study aimed to test the hypothesis that a history of abuse is associated with unsuccessful clinical outcomes of EDBE through an effect mediated by impulsivity. METHODS: We assessed patients older than 15 years, 3 months with EDBE at inclusion and at 1 year. Recovery was defined as the absence of eating disorders at 1 year. A mediation analysis was performed by means of structural equation modelling. RESULTS: = 38). Contrary to our assumption, a history of abuse was not associated with the absence of recovery of EDBE at 1 year. Factors unfavourable for achieving recovery were anxiety disorders (odds ratio [OR] 0.41), vomiting (OR 0.39), physical hyperactivity (OR 0.29), negative urgency and a lack of perseverance (OR 0.85 for both). Only positive urgency was positively associated with recovery (OR 1.25). LIMITATIONS: We excluded 219 patients lost to the 1-year follow-up. CONCLUSION: Our findings may help to deconstruct the empirical belief that traumatic events may interfere with the successful course of treatment for eating disorders. A high level of positive urgency may be associated with more receptivity to care.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".