Rationale, design and baseline characteristics of the effect of ticagrelor on health outcomes in diabetes mellitus patients Intervention study
Bibliographic record
Abstract
In the setting of prior myocardial infarction, the oral antiplatelet ticagrelor added to aspirin reduced the risk of recurrent ischemic events, especially, in those with diabetes mellitus. Patients with stable coronary disease and diabetes are also at elevated risk and might benefit from dual antiplatelet therapy. The Effect of Ticagrelor on Health Outcomes in diabEtes Mellitus patients Intervention Study (THEMIS, NCT01991795) is a Phase 3b randomized, double-blinded, placebo-controlled trial of ticagrelor vs placebo, on top of low dose aspirin. Patients ≥50 years with type 2 diabetes receiving anti-diabetic medications for at least 6 months with stable coronary artery disease as determined by a history of previous percutaneous coronary intervention, bypass grafting, or angiographic stenosis of ≥50% of at least one coronary artery were enrolled. Patients with known prior myocardial infarction (MI) or stroke were excluded. The primary efficacy endpoint is a composite of cardiovascular death, myocardial infarction, or stroke. The primary safety endpoint is Thrombolysis in Myocardial Infarction major bleeding. A total of 19 220 patients worldwide have been randomized and at least 1385 adjudicated primary efficacy endpoint events are expected to be available for analysis, with an expected average follow-up of 40 months (maximum 58 months). Most of the exposure is on a 60 mg twice daily dose, as the dose was lowered from 90 mg twice daily partway into the study. The results may revise the boundaries of efficacy for dual antiplatelet therapy and whether it has a role outside acute coronary syndromes, prior myocardial infarction, or percutaneous coronary intervention.
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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 | 0.030 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.012 |
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".