Role of phenotypic and genetic testing in managing clopidogrel therapy
Bibliographic record
Abstract
The P2Y12 inhibitors, clopidogrel, prasugrel, and ticagrelor, are administered in fixed doses without laboratory monitoring. Randomized trials in acute coronary syndrome have shown that prasugrel and ticagrelor are more effective than standard-dose clopidogrel. Nonetheless, standard-dose clopidogrel remains widely used because it causes less bleeding and is less expensive. Patients treated with standard-dose clopidogrel have substantial variability in platelet inhibition, which is partly explained by genetic polymorphisms encoding CYP2C19, the hepatic enzyme involved in biotransformation of clopidogrel to its active metabolite. Some advocate tailoring P2Y12 inhibitor therapy according to the results of routine laboratory testing. Although there is good evidence for analytic, biological, and clinical validity of several phenotypic and genotypic biomarkers, the benefit of a management strategy that incorporates routine biomarker testing over standard of care without such testing remains unproven. Appropriately designed, adequately powered trials are needed but face the challenges of feasibility, cost, and the progressive switch from clopidogrel to prasugrel or ticagrelor.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".