Glycaemic control and cardiovascular risk factor management in patients with diabetes with and without coronary artery disease: insights from the diabetes mellitus status in Canada survey
Bibliographic record
Abstract
Aims: Current diabetes guidelines recommend an individualized approach to glycaemic control. There are limited data on the contemporary and comprehensive management of patients with diabetes in relation to coronary artery disease (CAD). Methods and results: The Diabetes Mellitus Status in Canada (DM-SCAN) survey included 5123 patients with type 2 diabetes seen in primary care in November 2012. Primary care physicians (PCPs) collected clinical data and specified the A1C target for each patient on standardized forms. We compared management strategies and achievement of treatment targets in patients with and without CAD. Among the 4994 patients with data on CAD history, 22.5% had CAD. Primary care physicians were more likely to select a higher A1C target for patients with CAD (≤7.5 or ≤8.0%) versus without (≤7.0%). There was no difference in median A1C or in the proportion of patients with A1C ≤7.0% between the two groups. Compared with the group without known CAD, patients with CAD had a higher reported prevalence of hypoglycaemia in the preceding 6 months; more frequently received aspirin, statins, ACE inhibitors, or angiotensin receptor blockers, and were more likely to achieve blood pressure and low-density lipoprotein-cholesterol targets. Only 15.4 and 12.0% of patients with and without CAD (P = 0.002), respectively, achieved all three guideline-recommended targets. Conclusion: Compared with patients with diabetes without CAD, those with CAD more frequently had a less stringent A1C target selected by their PCPs but achieved similar glycaemic control. Overall, risk factor management remained suboptimal in both groups. There remains an important opportunity to improve the care and outcome of patients with 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".