Abstract P131: Importance of Algorithm-Consistent Warfarin Dosing in the Quality of Anticoagulation Control in Atrial Fibrillation: A Multilevel Analysis
Bibliographic record
Abstract
Background: The quality of anticoagulation control in AF patients on warfarin varies between countries, but the reasons are unclear. We determined the effect of algorithm-based warfarin dosing on anticoagulation control after adjusting for patient, center, and country characteristics. Patients: RE-LY trial patients with AF on warfarin. Centers with >5 patients on warfarin were included. Design: We tracked INR measurements and warfarin doses in patients. Dose adjustments were considered algorithm-consistent if they were within 5% of the recommendation made in the RE-LY warfarin nomogram. We developed a multilevel linear regression model with patients (1st level) nested in centers (2nd level), and centers nested in countries (3rd level), and examined the effect of algorithm-consistent dosing on Time in Therapeutic Range (TTR) of the INR. Results: A total of 4577 patients (62% male) from 402 centers and 40 countries were included. Considerable regional variation in mean TTR was found, ranging from 54 ± 22% in East Asian countries to 73 ± 15% in North European countries. The degree of algorithm-consistent warfarin dosing correlated with mean country TTR (r 2 =0.6) (Fig.). After adjusting for patient, center, and country variables, algorithm-consistent warfarin dosing was found to strongly predict TTR; a 1% increase in consistency with algorithm-based dosing increased mean TTR by 0.67% [0.61-0.73%, p<0.001]. Conclusion: Algorithm-consistent warfarin dosing is predictive of anticoagulation control after adjusting for patient, center, and country factors. There is potential to improve the quality of anticoagulation control by simple algorithm-based warfarin dosing.
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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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".