Low-dose aspirin and rivaroxaban combination therapy to overcome aspirin non-sensitivity in patients with vascular disease
Bibliographic record
Abstract
Approximately 20% of vascular patients treated with acetyl salicylic acid (i.e., aspirin) demonstrate less than expected platelet inhibition – putting them at a four-fold increased risk of adverse cardiovascular events. Low-dose rivaroxaban (2.5 mg twice daily) in combination with low-dose aspirin has been shown to reduce adverse cardiovascular and limb events when compared to aspirin alone. In this study, light transmission aggregometry was used to measure arachidonic acid-induced platelet aggregation to evaluate the potential of combining low-dose rivaroxaban and aspirin in attenuating or overcoming aspirin non-sensitivity. In the discovery phase, 83 patients with peripheral arterial disease (PAD) taking 81 mg aspirin daily were recruited from the outpatient vascular surgery clinic at St Michael's Hospital between January to September 2021. 19 (23%) were determined to be non-sensitive to aspirin. After ex-vivo addition of 2.5 mg dosage equivalent of rivaroxaban, aspirin non-sensitivity was overcome in 11 (58%) of these 19 patients. In the validation phase, 58 patients with cardiovascular risk factors who were not previously prescribed aspirin were recruited. In this group, ex-vivo addition of 2.5 mg dosage equivalent of rivaroxaban significantly reduced arachidonic acid-induced platelet aggregation in the presence of aspirin. These results demonstrate the potential for low-dose rivaroxaban to overcome aspirin non-sensitivity in patients with PAD. Further studies are needed to evaluate and confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 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".