2362Impact of diabetes on 5-year clinical outcomes in stable coronary artery disease, across multiple geographical regions and ethnicities. Insights from the CLARIFY registry
Bibliographic record
Abstract
Background: In contrast to acute myocardial infarction, limited data are available for the effect of diabetes mellitus on outcomes in contemporary cohorts with stable coronary disease (CAD). The prevalence and prognostic impact of diabetes may vary according to geographical region and ethnicity. Methods: CLARIFY is an observational registry of patients with stable CAD enrolled in 45 countries (Europe, Asia, America, Middle East, Australia, Africa) in 2009/2010. Stable CAD was defined as ≥1 of myocardial infarction (>3 months), evidence of coronary stenosis >50% on angiography, proven symptomatic myocardial ischaemia or previous revascularization procedure (>3 months). Yearly follow-ups were done for 5 years. Results: Among 32,703 patients enrolled, 9502 (29%) had diabetes mellitus; regional prevalence ranged from 24.5% in UK, South Africa, Canada and Australia to 59.7% in the Middle East. There were important differences in baseline characteristics and treatment according to the presence of diabetes. Patients with diabetes were more often female, older, and less frequently white than patients without diabetes. At 5 years, clinical outcomes (Table) adjusted for demographics, clinical history, NYHA class, LVEF, and creatinine, were markedly worse among patients with diabetes. There were modest differences according to geography and ethnicity.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".