Diabetic Ketoacidosis and Mortality in People With Type 1 Diabetes and Eating Disorders
Bibliographic record
Abstract
OBJECTIVE To determine the risk of diabetic ketoacidosis (DKA) and all-cause mortality among adolescents and young adults with type 1 diabetes with and without an eating disorder. RESEARCH DESIGN AND METHODS With use of population-level health care administrative data covering the entire population of Ontario, Canada, all people with type 1 diabetes aged 10–39 years as of January 2014 were identified. Individuals with a history of eating disorders were age- and sex-matched 10:1 with individuals without eating disorders. All individuals were followed for 6 years for hospitalization/emergency department visits for DKA and for all-cause mortality. RESULTS We studied 168 people with eating disorders and 1,680 age- and sex-matched people without eating disorders. Among adolescents and young adults with type 1 diabetes, 168 (0.8%) had a history of eating disorders. The crude incidence of DKA was 112.5 per 1,000 patient-years in people with eating disorders vs. 30.8 in people without eating disorders. After adjustment for baseline differences, the subdistribution hazard ratio for comparison of people with and without eating disorders was 3.30 (95% CI 2.58–4.23; P < 0.0001). All-cause mortality was 16.0 per 1,000 person-years for people with eating disorders vs. 2.5 for people without eating disorders. The adjusted hazard ratio was 5.80 (95% CI 3.04–11.08; P < 0.0001). CONCLUSIONS Adolescents and young adults with type 1 diabetes and eating disorders have more than triple the risk of DKA and nearly sixfold increased risk of death compared with their peers without eating disorders.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".