The quality of pharmacist-led community warfarin management across 2 provinces in Canada: A cross-sectional observational study
Bibliographic record
Abstract
Background: Guidelines for anticoagulation management services recommend personnel be specially trained in warfarin management and suggest using tools such as decision-support software. To date, there have been no Canadian studies documenting the quality of warfarin management using a similar guideline recommended approach. Methods: A cross-sectional, retrospective observational study was conducted to measure the quality of pharmacist-led warfarin management using point-of-care international normalized ratio (INR) testing and decision-support software in various ambulatory settings in Canada. Settings included 4 family health teams in Ontario and 40 community pharmacies across Nova Scotia. Quality was measured using time in therapeutic range (TTR) and was reported in 3 manners: mean TTR, median TTR and time-weighted mean TTR. Results: The primary outcome included 963 patients. The combined mean and median TTR for the 2019 Ontario family health teams and Nova Scotia pharmacies was 74.2% and 77.3% (interquartile range 64%-87.9%), respectively. The time-weighted mean TTR was 76.3%. Discussion: To the best of our knowledge, the TTR achieved by this model of care is the highest reported in Canadian general practice. Since Thrombosis Canada defines good-quality warfarin management as a TTR of 60% or greater, and many studies have reported an association between higher TTR values and lower rates of thrombosis and hemorrhage, this model of care may have significant benefits for patients. Conclusion: 2024;157:xx-xx.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".