Bibliographic record
Abstract
The efficacy of aspirin to prevent thrombotic events in cardiovascular patients is well established, with >100 randomized trials having been conducted in high-risk patients and demonstrating a reduction in vascular death of approximately 15% and a further reduction in non-fatal vascular events of approximately 30%. While the benefit of aspirin is undisputed, it is also known that aspirin is associated with a dose-dependent increase in the risk of bleeding. It follows that most treatment guidelines advocate the use of the lowest aspirin dose effective in preventing thrombotic complications to minimize the risk of major bleeding. From this, a need for monitoring of aspirin therapy has emerged and prompted the development and investigation of numerous assays of platelet function. The intention behind monitoring of aspirin's antithrombotic effects is to maximize benefit and to personalize treatment based on individual patient characteristics. This article reviews the recent literature on the usefulness of platelet function testing in patients requiring aspirin; the variability of platelet reactivity in patients taking aspirin and its clinical impact; the potential mechanisms underlying suboptimal platelet inhibition by aspirin and future directions in terms of management of aspirin therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".