Validation of Warfarin Pharmacogenetic Algorithms in Clinical Practice
Bibliographic record
Abstract
AIM: The goal of this study was to evaluate the performance of four warfarin pharmacogenetic algorithms in a real clinical setting, namely the algorithms of Gage et al., Michaud et al., Wadelius et al. and the International Warfarin Pharmacogenetics Consortium algorithm. PATIENTS & METHODS: Data was obtained retrospectively for 605 patients who had initiated warfarin therapy at the Montreal Heart Institute. Warfarin dosing and International Normalized Ratio history were obtained from hospital charts and CYP2C9 and VKORC1 polymorphisms were genotyped. RESULTS: The four algorithms produced similar accuracy with mean absolute error ranging from 1.36-1.52 mg/day and adjusted R(2) from 40-44%. Gage's algorithm and Wadelius' algorithm predicted the largest proportion of patients within ± 20% of their observed stable warfarin dose. For patients requiring low doses, Gage's algorithm provided the highest proportion of patients within ideal dose range (36.3%), while Wadelius' algorithm performed the best for patients requiring high doses (37.3% of patients within ideal dose range). CONCLUSION: Our study demonstrates the value of published pharmacogenetic dosing algorithms for the prediction of warfarin doses, in particular for patients with low or high therapeutic dose requirements.
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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 | 0.065 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".