Impact of treatment strategies on outcomes in patients with stable coronary artery disease and type 2 diabetes mellitus according to presenting angina severity: A pooled analysis of three federally-funded randomized trials
Bibliographic record
Abstract
BACKGROUND AND AIMS: The impact of treatment strategies on outcomes in patients with stable coronary artery disease (CAD) and type 2 diabetes mellitus (T2DM) according to presenting angina has not been rigorously assessed. METHODS: We performed a patient-level pooled-analysis (n = 5027) of patients with stable CAD and T2DM randomized to optimal medical therapy [OMT], percutaneous coronary intervention [PCI] + OMT, or coronary artery bypass grafting [CABG] + OMT. Endpoints were death/myocardial infarction (MI)/stroke, post-randomization revascularization (both over 5 years), and angina control at 1 year. RESULTS: Increasing severity of baseline angina was associated with higher rates of death/MI/stroke (p = 0.009) and increased need for post-randomization revascularization (p = 0.001); after multivariable adjustment, only association with post-randomization revascularization remained significant. Baseline angina severity did not influence the superiority of CABG + OMT to reduce the rate of death/MI/stroke and post-randomization revascularization compared to other strategies. CABG + OMT was superior for angina control at 1 year compared to both PCI + OMT and OMT alone but only in patients with ≥ Class II severity at baseline. Comparisons between PCI + OMT and OMT were neutral except that PCI + OMT was superior to OMT for reducing the rate of post-randomization revascularization irrespective of presenting angina severity. CONCLUSIONS: Presenting angina severity did not influence the superiority of CABG + OMT with respect to 5-year rates of death/MI/stroke and need for post-randomization revascularization. Presenting angina severity minimally influenced relative benefits for angina control at 1 year.
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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 | 0.029 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.032 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".