Risk factors for binge eating and purging eating disorders: Differences based on age of onset
Bibliographic record
Abstract
OBJECTIVE: To (1) determine whether childhood risk factors for early onset binge eating and purging eating disorders also predict risk for later-onset binge eating and purging disorders, and (2) compare the utility of childhood and early adolescent variables in predicting later-onset disorders. METHOD: Participants (N = 1,383) were drawn from the Western Australian Pregnancy Cohort (Raine) Study, which has followed children from pre-birth to age 20. Eating disorders were assessed when participants were aged 14, 17, and 20. Risk factors for early onset eating disorders have been reported previously (Allen et al., J Am Acad Child Psychiat, 48, 800-809, 2009). This study used logistic regression to determine whether childhood risk factors for early onset disorders, as previously identified, would also predict risk for later-onset disorders (n = 145). Early adolescent predictors of later-onset disorders were also examined. RESULTS: Consistent with early onset cases, female sex and parent-perceived child overweight at age 10 were significant multivariate predictors of binge eating and purging disorders with onset in later adolescence. Eating, weight, and shape concerns at age 14 were also significant in predicting later-onset disorders. In the final stepwise multivariate model, female sex and eating, weight, and shape concerns at age 14 were significant in predicting later-onset eating disorders, while parent-perceived child overweight at age 10 was not. DISCUSSION: There is overlap between risk factors for binge eating and purging disorders with early and later onset. However, childhood exposures may be more important for early than later onset cases.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".