Performance of Commercial Platforms for Rapid Genotyping of Polymorphisms Affecting Warfarin Dose
Bibliographic record
Abstract
Initiation of warfarin therapy is associated with bleeding owing to its narrow therapeutic window and unpredictable therapeutic dose. Pharmacogenetic-based dosing algorithms can improve accuracy of initial warfarin dosing but require rapid genotyping for cytochrome P-450 2C9 (CYP2C9) *2 and *3 single nucleotide polymorphisms (SNPs) and a vitamin K epoxide reductase (VKORC1) SNP. We evaluated 4 commercial systems: INFINITI analyzer (AutoGenomics, Carlsbad, CA), Invader assay (Third Wave Technologies, Madison, WI), Tag-It Mutation Detection assay (Luminex Molecular Diagnostics, formerly Tm Bioscience, Toronto, Canada), and Pyrosequencing (Biotage, Uppsala, Sweden). We genotyped 112 DNA samples and resolved any discrepancies with bidirectional sequencing. The INFINITI analyzer was 100% accurate for all SNPs and required 8 hours. Invader and Tag-It were 100% accurate for CYP2C9 SNPs, 99% accurate for VKORC1 -1639/3673 SNP, and required 3 hours and 8 hours, respectively. Pyrosequencing was 99% accurate for CYP2C9 *2, 100% accurate for CYP2C9 *3, and 100% accurate for VKORC1 and required 4 hours. Current commercial platforms provide accurate and rapid genotypes for pharmacogenetic dosing during initiation of warfarin therapy.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".