Pharmacogenomics in Stroke and Cardiovascular Disease: State of the Art
Bibliographic record
Abstract
There is considerable interindividual variability in the response to antiplatelet and anticoagulant therapies, and this variation may be attributable to genetic variants. There has been an increased understanding of the genetic architecture of stroke and cardiovascular disease, which has been driven by advancements in genomic technologies and this has raised the possibility of more targeted pharmaceutical treatments. Pharmacogenetics promises to use a patient's genetic profile to treat those who are more likely to benefit from a particular intervention by selecting the best possible therapy. Although there are numerous studies indicating strong evidence for the effect of specific genotypes on the outcomes of vascular drugs, the adoption of pharmacogenetic testing in clinical practice has been slow. This resistance may stem from sometimes conflicting findings among pharmacogenetic studies, a lack of stroke-specific randomized controlled trials to test the effectiveness of genetically-guided therapies, and the practical and cost-effective implementation of genetic testing within the clinic. Thus, this review provides an overview of the genetic variants that influence the individual responses to aspirin, clopidogrel, warfarin and statins and the different methods for pharmacogenetic testing and guidelines for clinical implementation for stroke 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".