Eating disorders in adolescent females with and without type 1 diabetes: cross sectional study
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of eating disorders in adolescent females with type 1 diabetes mellitus compared with that in their non-diabetic peers. DESIGN: Cross sectional case-control led study. SETTING: Diabetes clinics and schools in three Canadian cities. SUBJECTS: 356 females aged 12-19 with type 1 diabetes and 1098 age matched non-diabetic controls. MAIN OUTCOME MEASURE: Eating disorders meeting Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. RESULTS: Eating disorders that met DSM-IV criteria were more prevalent in diabetic subjects (36, 10%) than in non-diabetic controls (49, 4%) (odds ratio 2.4, 95% confidence interval 1.5 to 3.7; P<0.001). Subthreshold eating disorders were also more common in those with diabetes (49, 14%) than in controls (84, 8%) (odds ratio 1.9, 95% confidence interval 1.3 to 2.8; P<0.001). Mean haemoglobin A(1c) concentration was higher in diabetic subjects with an eating disorder (9.4% (1.8)) than in those without (8.6% (1.6)), P=0.04). CONCLUSIONS: DSM-IV and subthreshold eating disorders are almost twice as common in adolescent females with type 1 diabetes as in their non-diabetic peers. In diabetic subjects, eating disorders are associated with insulin omission for weight loss and impaired metabolic control.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".