Lower achievement of guideline recommended care in Canadian adults with early-onset diabetes: A population-based cohort study
Bibliographic record
Abstract
AIMS: Adults with early-onset diabetes (age < 40 years) have an increased risk of complications, and it is unclear whether they are receiving guideline recommended care. We compared the frequency and results of haemoglobin A1c (HbA1c) testing in adults with early-onset and usual-onset diabetes and assessed factors related to guideline concordance. METHODS: Population-level databases from Alberta, Canada (∼4.5 million) were used to identify adults with incident diabetes. The cohort was stratified by age at diagnosis (< 40 vs. ≥ 40 years) and then followed for 365 days for HbA1c testing. Adjusted multivariable analyses were used to identify clinical and sociodemographic factors associated with guideline concordance. RESULTS: Among 23,643 adults with incident diabetes (mean age 54.1 ± 15.4 years; 42.1 % female), 18.9 % had early-onset diabetes. Early-onset diabetes was associated with lower frequency of testing (adjusted odds ratio (aOR), 0.80; 95 % CI 0.70-0.90) and above target glycaemic levels compared to usual-onset diabetes (aOR, 1.45; 95 % CI 1.29-1.64). Factors associated with guideline concordant frequency of HbA1c testing were rural residence and insulin use. CONCLUSIONS: In our universal care setting with premium-free health care, early-onset diabetes was associated with lower rates of HbA1c testing and sub-optimal glycaemic control compared to those with usual-onset diabetes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".