Applicability and impact of the COMPASS trial in a Canadian population of patients with atherosclerotic disease
Bibliographic record
Abstract
BACKGROUND AND AIMS: In the COMPASS trial, low-dose rivaroxaban with aspirin improved cardiovascular outcomes in patients with atherosclerotic cardiovascular disease (ASCVD). We aimed to assess the potential clinical implications of this therapy in a generalizable population. METHODS AND RESULTS: A retrospective cohort of adults with ASVCD was formed using healthcare administrative databases in Alberta, Canada (population 4.4 million). Patients with a new diagnosis between 2008 and 2019 formed the epidemiological cohort (n = 224,600) and those with long-term follow-up (>5 years) formed the outcomes cohort (n = 232,460). The primary outcome of major adverse cardiovascular events (MACE) was assessed and categorized based on the COMPASS trial eligibility. In the outcomes cohort, 77% had only coronary artery disease, 15% had only peripheral artery disease, and 8% had both. Of those, 37% met the COMPASS trial eligibility criteria, 36% met exclusion criteria and 27% did not meet inclusion criteria. Over a median of 7.8 years, the COMPASS exclusion group demonstrated the highest rate of MACE (5.9 per 100 person-years), following by the eligible group and the group that did not meet COMPASS inclusion criteria (3.1 and 1.4 per 100 person-years respectively). The expected net clinical benefit of antithrombotic therapy in the eligible group was 5.6 fewer events per 1000 person-years. CONCLUSIONS: In a real-world population of 4.4 million adults, there are roughly 20,000 new cases of ASVCD diagnosed yearly, with ∼40% being eligible for the addition of low-dose rivaroxaban therapy to antiplatelet therapy. The theoretical implementation of dual antithrombotic treatment in this population could result in a substantial reduction in cardiovascular morbidity and mortality.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".