Effect of <i> <scp>CYP</scp> 4F2 </i> , <i> <scp>VKORC</scp> 1 </i> , and <i> <scp>CYP</scp> 2C9 </i> in Influencing Coumarin Dose: A Single‐Patient Data Meta‐Analysis in More Than 15,000 Individuals
Bibliographic record
Abstract
The cytochrome P450 (CYP)4F2 gene is known to influence mean coumarin dose. The aim of the present study was to undertake a meta-analysis at the individual patients level to capture the possible effect of ethnicity, gene-gene interaction, or other drugs on the association and to verify if inclusion of CYP4F2*3 variant into dosing algorithms improves the prediction of mean coumarin dose. We asked the authors of our previous meta-analysis (30 articles) and of 38 new articles retrieved by a systematic review to send us individual patients' data. The final collection consists of 15,754 patients split into a derivation and validation cohort. The CYP4F2*3 polymorphism was consistently associated with an increase in mean coumarin dose (+9% (95% confidence interval (CI) 7-10%), with a higher effect in women, in patients taking acenocoumarol, and in white patients. The inclusion of the CYP4F2*3 in dosing algorithms slightly improved the prediction of stable coumarin dose. New pharmacogenetic equations potentially useful for clinical practice were derived.
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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 | 0.011 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.035 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".