Platelet reactivity in stable cardiovascular patients with chronic kidney disease
Bibliographic record
Abstract
The study aimed to evaluate antiplatelet drug responsiveness in stable outpatients with cardiovascular disease and chronic kidney disease (CKD) and examine whether impaired antiplatelet drug responsiveness is associated with worse clinical outcomes in this population. Stable cardiovascular patients (n = 771) were enrolled at least one month after an acute ischemic atherothrombotic event. Antiplatelet drug responsiveness was assessed with specific assays (serum TxA2 for aspirin, the VASP assay for clopidogrel) and other aggregation-based assays using different agonists. All patients were followed until the first occurrence of a major adverse cardiovascular event. The 133 CKD patients were found to have higher activity of von Willebrand factor and higher fibrinogen levels. After a median follow-up of 33 months, 88 events occurred in patients without CKD and 31 events in patients with CKD (5.0 events and 8.7 events per 100 patient years, respectively, HR = 1.75 (95% CI 1.16–2.63; p = 0.008). The prevalence of poor aspirin and clopidogrel responsiveness and high platelet reactivity as assessed with different aggregation-based assays was similar in patients with estimated GFR ≥ 60 ml/min, 45–59 ml/min, and < 45 ml/min. No significant interaction for CKD vs. non-CKD was observed for events occurrence in patients with or without high platelet reactivity on several assays, with the exception of collagen-induced aggregation. In stable cardiovascular patients, CKD is not associated with higher platelet reactivity. Decreased antiplatelet drug responsiveness is not associated with worse clinical outcomes in CKD patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".