Targeting platelets for prevention and treatment of cardiovascular disease
Bibliographic record
Abstract
Platelets play an important role in the development of thrombosis, atherosclerosis, hypertension, heart attack and stroke. As a result, pharmacologic interventions that influence platelet functions, such as adhesion, aggregation and the release of different factors, are considered useful for the prevention and treatment of cardiovascular disease. Although classical anti-platelet agents have proven beneficial effects for the treatment of some specific cardiovascular diseases, there are limitations for their use as these drugs target platelet function directly. In contrast, newly developed anti-platelet agents have broad applications for the treatment of cardiovascular disease as they not only influence platelet function but are also considered to affect cardiac and vascular smooth muscle cell functions. Natural food products and nutraceutical agents also appear to modify cardiovascular abnormalities by affecting various platelet functions; however, the mechanisms of their actions remain to be investigated. Accordingly, this article is focused to discuss emerging pharmacologic, nutritional and nutraceutical interventions that may influence the prevention or progression of a broad range of cardiovascular diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".