Estimation of Warfarin Maintenance Dose Based on VKORC1 (−1639 G>A) and CYP2C9 Genotypes
Bibliographic record
Abstract
BACKGROUND: CYP2C9 polymorphisms are associated with decreased S-warfarin clearance and lower maintenance dosage. Decreased expression of VKORC1 resulting from the -1639G>A substitution has also been implicated in lower warfarin dose requirements. We investigated the additional contribution of this polymorphism to the variance in warfarin dose. METHODS: Sixty-five patients with stable anticoagulation were genotyped for CYP2C9 and VKORC1 with Tag-It allele-specific primer extension technology. Plasma S-warfarin concentrations and warfarin maintenance dose were compared among patients on the basis of the VKORC1 -1639G>A genotype. RESULTS: Eighty percent of CYP2C9*1/*1 patients stabilized on <4.0 mg/day warfarin had at least 1 VKORC1 -1639A allele. Mean warfarin doses (SD) were 6.7 (3.3), 4.3 (2.2), and 2.7 (1.2) mg/day for patients with the VKORC1 -1639GG, GA, and AA genotypes, respectively. Steady-state plasma concentrations of S-warfarin were lowest in patients with the VKORC1 -1639AA genotype and demonstrated a positive association with the VKORC1 -1639G allele copy number (trend P = 0.012). A model including VKORC1 and CYP2C9 genotypes, age, sex, and body weight accounted for 61% of the variance in warfarin daily maintenance dose. CONCLUSIONS: The VKORC1 -1639A allele accounts for low dosage requirements of most patients without a CYP2C9 variant. Higher plasma S-warfarin concentrations corresponding to increased warfarin maintenance dosages support a hypothesis for increased expression of the VKORC1 -1639G allele. VKORC1 and CYP2C9 genotypes, age, sex, and body weight account for the majority of variance in warfarin dose among our study population.
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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 | 0.001 |
| 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) | 0.002 | 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".