Predictors of mortality in hospital survivors with type 2 diabetes mellitus and acute coronary syndromes
Bibliographic record
Abstract
AIM: To define the predictors of long-term mortality in patients with type 2 diabetes mellitus and recent acute coronary syndrome. METHODS AND RESULTS: A total of 7226 patients from a randomized trial, testing the effect on cardiovascular outcomes of the dual peroxisome proliferator-activated receptor agonist aleglitazar in patients with type 2 diabetes mellitus and recent acute coronary syndrome (AleCardio trial), were analysed. Median follow-up was 2 years. The independent mortality predictors were defined using Cox regression analysis. The predictive information provided by each variable was calculated as percent of total chi-square of the model. All-cause mortality was 4.0%, with cardiovascular death contributing for 73% of mortality. The mortality prediction model included N-terminal proB-type natriuretic peptide (adjusted hazard ratio = 1.68; 95% confidence interval = 1.51-1.88; 27% of prediction), lack of coronary revascularization (hazard ratio = 2.28; 95% confidence interval = 1.77-2.93; 18% of prediction), age (hazard ratio = 1.04; 95% confidence interval = 1.02-1.05; 15% of prediction), heart rate (hazard ratio = 1.02; 95% confidence interval = 1.01-1.03; 10% of prediction), glycated haemoglobin (hazard ratio = 1.11; 95% confidence interval = 1.03-1.19; 8% of prediction), haemoglobin (hazard ratio = 1.01; 95% confidence interval = 1.00-1.02; 8% of prediction), prior coronary artery bypass (hazard ratio = 1.61; 95% confidence interval = 1.11-2.32; 7% of prediction) and prior myocardial infarction (hazard ratio = 1.40; 95% confidence interval = 1.05-1.87; 6% of prediction). CONCLUSION: In patients with type 2 diabetes mellitus and recent acute coronary syndrome, mortality prediction is largely dominated by markers of cardiac, rather than metabolic, dysfunction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".