Pharmacogenomics and its Role in Cardiovascular Diseases: A Narrative Literature Review
Why this work is in the frame
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Bibliographic record
Abstract
Pharmacogenomics has transformed the way we approach the treatment of the most common diseases worldwide, especially cardiovascular. In this article, we highlight the main categories of drugs involved in major cardiovascular diseases (CVD), related genetic variability and their effects on metabolism in each case of contrastive operability. This not only explains disparities in treatment outcomes but also unfolds customised management based on genomic studies to improve efficiency and limit side effects. Genetic variations have been identified that impact the efficacy, safety, and adverse effects of drugs commonly used in the treatment of CVD, such as Angiotensin converting Enzyme Inhibitor (ACEI), Angiotensin Receptor Blocker (ARBs), calcium channel blockers, antiplatelet agents, diuretics, statins, beta-blockers, and anticoagulants. It discusses the impact of genetic polymorphisms on drug metabolism, efficacy, and adverse reactions, highlighting the importance of genetic testing in optimizing treatment outcomes. Pharmacogenomics holds immense potential for revolutionizing the management of CVD by enabling personalized medicine approaches tailored to individual genetic profiles. However, challenges such as clinical implementation, cost-effectiveness, and ethical considerations need to be addressed to completely incorporate pharmacogenomic testing into standard clinical practice. Continued research and clinical diligence are required for the utilization of pharmacogenomics to improve therapeutic outcomes and reduce the burden of CVD globally.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it