Recent Improvements in Attainment of the Hemoglobin A1c Target of ≤7.0% Among Adults with Type 1 Diabetes in Ontario: A Retrospective Cohort Study
Bibliographic record
Abstract
Aims: We evaluated attainment of the hemoglobin A1c (HbA1c) target of ≤7.0%, its temporal trends, and associated factors among adults with type 1 diabetes in Ontario, Canada, using administrative data. Methods: We conducted a retrospective cohort study, including Ontarians with type 1 diabetes ≥18 years old with ≥1 HbA1c test between April 1, 2012 (fiscal year 2013), and March 31, 2023. Generalized estimating equations were used to determine probabilities of meeting the HbA1c target, as well as associations between fiscal year and individual-, physician-, and system-level factors on odds of meeting the target. Results: Among 28,827 adults with type 1 diabetes [14,385 (49.9%) female, 17,998 (62.4%) pump users], with median age at index of 25 years [interquartile range (IQR) 18–37] and median diabetes duration of 12 years [6–18], there were 474,714 HbA1c tests [median 2/individual/year (IQR: 1–3)]. The model-estimated probability of meeting the HbA1c target of ≤7.0% was 22.1% (95% confidence interval, CI: 21.6 to 22.5) in 2013, remained stable until 2020, and increased to 34.7% (95% CI: 34.3 to 35.2) in 2023. The age- and sex-adjusted odds ratio for meeting the target in 2023 versus 2013 was 1.87 (95% CI: 1.79 to 1.96). Young adults (18–25 years), diabetic ketoacidosis, greater comorbidity, and receiving diabetes care from a nonspecialist physician were associated with reduced odds of meeting the HbA1c target. Conclusions: One-third of adults with type 1 diabetes in Ontario met the recommended HbA1c target of ≤7.0% in 2023, with improvement noted since 2021, which may be due to advanced technologies or effects of the COVID-19 pandemic.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".