Predictors of type 1 diabetes mellitus outcomes in young adults after transition from pediatric care
Bibliographic record
Abstract
BACKGROUND: Type 1 diabetes (T1D) is a common chronic disease. Poor health outcomes are often noted after transfer to adult health care. It is important to determine the predictors of such outcomes to decrease morbidity and mortality. METHODS: The present retrospective study included patients followed for ≥1 year before and ≥1 year after transfer to adult care in a Canadian tertiary diabetes center. Data including demographics, education, comorbidity and pediatric diabetes management-related factors were analyzed as possible independent predictors of adult HbA1c, number of adult diabetes-related hospitalizations, and clinic visits. RESULTS: In all, 102 youths were followed to a mean (±SD) age of 21.8 ± 1.5 years. Predictors of mean adult HbA1c using linear regression were the presence of any comorbidity (0.71%; 95% confidence interval [CI] 0.15-1.27; P = 0.01) and pediatric HbA1c (0.67% per 1% increase in HbA1c; 95% CI 0.51-0.84; P < 0.001). Predictors of hospitalization for hyperglycemia were a history of pediatric hospitalization for hyperglycemia (incidence rate ratio [IRR] 1.20; 95% CI 1.02-1.41; P = 0.029) and high school vs university education (IRR 3.13; 95% CI 1.12-8.73; P = 0.030). CONCLUSION: Young adults with complicated health histories and less education are more likely to experience poor diabetes outcomes in the years after transfer to adult care. These features may highlight youth requiring closer attention or may be targets for intervention.
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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 | 0.000 | 0.002 |
| 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 | 0.001 | 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".