A whole new ball game: a retrospective cohort study using healthcare administrative data to uncover predictors of timely transition from paediatric to adult type 1 diabetes care in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: To assess predictors of timely transition to adult diabetes care among individuals diagnosed with type 1 diabetes during childhood and adolescence. We hypothesised that older age at the last paediatric visit and urban residency would be predictors of timely transition. DESIGN: Retrospective cohort study using healthcare administrative data in a jurisdiction with a universal healthcare system. PARTICIPANTS: 2045 adolescents and young adults diagnosed with type 1 diabetes between the ages of 0.5 and 18 years. PRIMARY AND SECONDARY OUTCOME MEASURES: We ascertained age at the last paediatric diabetes visit (LPDV), age at the first adult diabetes visit (FADV) and transition duration, defined as the time between LPDV and FADV. Timely transition was defined as a transition duration of <1 year. Logistic regression models were fitted to assess predictors of timely transition. RESULTS: Only 31.3% of individuals saw an adult provider within 1 year of their LPDV. Each 1-year increase in the age at LPDV was associated with increased odds of timely transition (adjusted OR 1.82, 95% CI 1.71 to 1.93, p<0.001). Urban residency was also associated with increased odds of timely transition (adjusted OR 1.93, 95% CI 1.40 to 2.71, p<0.001). Sex and older age at diagnosis were not associated with timely transition in the multivariable regression models (p>0.05). CONCLUSIONS: Older age at the LPDV and urban residency are associated with increased odds of timely transition. Interventions should be developed to help keep adolescents engaged in paediatric care until an older age before referring them to adult diabetes care. Limitations of this study include unmeasured confounding and limited generalisability to non-universal healthcare systems.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".