Randomised comparison of a simple warfarin dosing algorithm versus a computerised anticoagulation management system for control of warfarin maintenance therapy
Bibliographic record
Abstract
Excellent control of the international normalised ratio (INR) is associated with improved clinical outcomes in patients receiving warfarin, and can be achieved by anticoagulation clinics but is difficult in general practice. Anticoagulation clinics have often used validated commercial computer systems to manage the INR, but these are not usually available to general practitioners. It was the objective of this study to perform a randomised trial of a simple one-step warfarin dosing algorithm against a widely used computerised dosing system. During the period of introduction of a commercial computerised warfarin dosing system (DAWN AC) to an anticoagulation clinic, patients were randomised to have warfarin dose adjustment done according to recommendations of the existing warfarin dosing algorithm or to those of the computerised system. The study tested if the computerised system was non-inferior to the existing algorithm for the primary outcome of time in therapeutic INR range of 2.0-3.0 (TTR), with a one-sided non-inferiority margin of 4.5%. There were 541 patients randomised to commercial computerised system and 527 to the algorithm. Median follow-up was 159 days. A dose recommendation was provided and followed in 91% of occasions for the computerised system and in 90% for the algorithm (p=0.03). The mean TTR was 71.0% (standard deviation [SD] 23.2) for the computerised system and 71.9% (SD 22.9) for the algorithm (difference 0.9% [95% confidence interval: -1.4% to 4.1%]; p-value for non-inferiority=0.002; p-value for superiority=0.34). In conclusion, similar maintenance control of the INR was achieved with a simple one-step dosing algorithm and a commercial computerised management system.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".