Pharmacogenetics of warfarin safety and effectiveness in children
Bibliographic record
Abstract
Objectives To assess the contribution of CYP2C9 / VKORC1 genotypes and variation in other genes involved in warfarin biotransformation and coagulation pathways to warfarin‐related outcomes in children. Furthermore, to validate the performance of a previously published pediatric pharmacogenetics‐dosing model when predicting the required dose in an independent cohort of children. Methods Clinical and genetic data was collected from 93 patients ≤18 years of age who received warfarin therapy. Genotyping was performed using a custom 96 SNP genotyping assay. Results Together, VKORC1 –1639G/A and CYP2C9 *2/*3 accounted for 21.1% of dose variability. There was a strong correlation (R 2 =0.64; P <0.001) between actual and predicted warfarin dose using a pediatric pharmacogenetics‐dosing model. VKORC1 genotype also had a significant impact on time to therapeutic INR ( P =0.047), time to INR>;4 ( P =0.028), and risk of over‐anticoagulation (INR>;4) during the initiation of therapy (odds ratio, 3.3; P =0.014). An additional variant in CYP2C9 (rs7089580) was significantly associated with warfarin dose in a multivariate model. Conclusions This study confirms the importance of VKORC1/CYP2C9 genotype for warfarin outcomes in children and validates a pediatric‐specific genotype‐based dosing algorithm. Research funding provided by CIHR‐DSEN and CFI.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".