Personalized medicine and antiplatelet therapy: ready for prime time?
Bibliographic record
Abstract
The concept of personalized medicine is receiving significant attention due to the greater awareness of the influence of genes to the drug effects. Single nucleotide polymorphisms (SNPs) in the DNA are the most frequent form of sequence variations in the human genome and appear to affect the efficacy and safety of many drugs. The term ‘pharmacogenetics’ was coined over 40 years ago with an ultimate goal of using the genetic makeup of an individual to predict drug response and efficacy.1–3 We are just at the beginning of a new era in personalized cardiovascular therapies. However there is little doubt that, in the near future, pharmacogenetic testing will become a valuable tool for a drug and dose selection and thus result in a more desirable benefit/risk ratio for drugs prescribed to patients. Over the past decades, the platelet has emerged as a major pathway involved in cardiovascular diseases. The platelet as a ‘drug target’ has spawned a variety of new drugs that have been shown in large-scale randomized trials to improve patient outcomes in acute coronary syndromes and following percutaneous revascularization procedures.4–6 Until recently aspirin, centred on the tromboxane pathway, was the only antiplatelet agent considered to be the gold standard for effectiveness in both primary and secondary prevention of atherothrombotic diseases.7 Although it continues to be used as the gold standard antiplatelet therapy, adenosine diphosphate (ADP) receptor antagonists and phosphodiesterase inhibitors in combination therapy appear to exert synergistic effects and provide added benefits among high-risk patients for cardiovascular disease.7,8 Nevertheless an important lesson that has emerged from number of trials is that antiplatelet potency per se does not necessarily guarantee enhanced clinical benefit or tolerability for a given patient.8–11 This may in part be due to the … *Corresponding author. Tel: +1 519 663 3553, Fax: +1 519 663 3232, Email: richard.kim{at}lhsc.on.ca
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.016 | 0.026 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".