Aspirin nonsensitivity in patients with vascular disease: Assessment by light transmission aggregometry (aspirin nonsensitivity in vascular patients)
Bibliographic record
Abstract
Background Aspirin is a key antiplatelet therapy for the prevention of thrombotic events in patients with cardiovascular disease. Studies suggest that ≈20% of patients with cardiac disease suffer from aspirin nonsensitivity, a phenomenon characterized by the inability of 81 mg aspirin to inhibit platelet aggregation and/or prevent adverse cardiovascular events. Objectives To investigate aspirin nonsensitivity in patients with vascular disease and assess the consequences of aspirin nonsensitivity. Methods One hundred fifty patients presenting to St. Michael’s Hospital’s outpatient clinics with evidence of vascular disease (peripheral arterial disease or carotid artery stenosis) and a previous prescription of 81 mg of aspirin were recruited in this study. Light transmission aggregometry with arachidonic acid induction was used to determine sensitivity to aspirin. Patients with a maximum aggregation ≥20% in response to arachidonic acid were considered aspirin nonsensitive, as per previous studies. Results Of the 150 patients recruited, 36 patients (24%) were nonsensitive to 81 mg of aspirin. Of these 36 nonsensitive patients, 30 patients provided a urine sample for urine salicyluric acid analysis (a major metabolite of aspirin). Urine analysis demonstrated that 14 patients were compliant and 16 were noncompliant with their aspirin therapy. Major adverse cardiovascular events and major adverse limb events were significantly higher in the nonsensitive patients compared to sensitive patients (hazard ratio, 3.68; P < 0.001). Conclusion These data highlight the high prevalence of aspirin nonsensitivity and noncompliance in patients with vascular disease and emphasizes the urgent need for improved medical management options for this patient population.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".