Anticoagulation: Optimal Initial Dose Adjustment of Warfarin in Orthopedic Patients
Bibliographic record
Abstract
BACKGROUND: Warfarin sodium is commonly prescribed for the prophylaxis and treatment of venous thromboembolism. Dosing algorithms have not been widely adopted because they require a fixed initial warfarin dose (eg, 5 mg) and are not tailored to other factors that may affect the international normalized ratio (INR). OBJECTIVE: To develop an algorithm that could predict a therapeutic warfarin dose based on drug interactions, INR response after the initial warfarin doses, and other clinical factors. METHODS: We used stepwise regression to quantify the relationship between these factors in patients beginning prophylactic warfarin therapy immediately prior to joint replacement. In the derivation cohort (n = 271), we separately modeled the therapeutic dose after 2 and 3 initial doses. We prospectively validated these 2 models in an independent cohort (n = 105). RESULTS: About half of the therapeutic dose variability was predictable after 3 days of therapy: R2 was 53% in the derivation cohort and 42% in the validation cohort. INR response after 3 warfarin doses (INR3) inversely correlated with therapeutic dose (p < 0.001). Intraoperative blood loss transiently, but significantly, elevated the postoperative INR values. Other significant (p < 0.03) predictors were the first and second warfarin doses (+7% and +6%, respectively, per 1 mg), and statin use (-15.0%). The model derived after 2 warfarin doses explained 32% of the variability in therapeutic dose. CONCLUSIONS: We developed and validated algorithms that estimate therapeutic warfarin doses based on clinical factors and INR response available after 2-3 days of warfarin therapy. The algorithms are implemented online at www.WarfarinDosing.org.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".