Prediction of the Onset of Disturbed Eating Behavior in Adolescent Girls With Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to identify predictors of the onset of disturbed eating behavior (DEB) in adolescent girls with type 1 diabetes. RESEARCH DESIGN AND METHODS: In this prospective study, participants completed the Children's Eating Disorder Examination interview and self-report measures at baseline and at four follow-up assessments over 5 years. Participants were 126 girls with type 1 diabetes, aged 9-13 years at baseline. Of the 101 girls who did not have DEB at baseline, 45 developed DEB during the follow-up period; the 38 for whom data were available for the assessment before onset of DEB were compared with 38 age-matched girls who did not develop DEB. DEB was defined as dieting for weight control, binge eating, self-induced vomiting, or the use of diuretics, laxatives, insulin omission, or intense exercise for weight control. RESULTS: Logistic regression indicated that a model including BMI percentile, weight and shape concern, global and physical appearance-based self-worth, and depression was significantly associated with DEB onset (chi(2) = 46.0, 5 d.f., P < 0.0001) and accounted for 48.2% of the variance. CONCLUSIONS: Even though scores on the measures were within the published normal range, the onset of DEB was predicted by higher depression and weight and shape concerns and lower global and physical appearance-based self-worth as well as higher BMI percentile 1-2 years earlier compared with those not developing DEB. Early interventions focused on helping girls with diabetes develop positive feelings about themselves, their weight and shape, and their physical appearance may have protective value.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".