Validation of Warfarin Pharmacogenetic Algorithms in Clinical Practice
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it