The Comparative Effectiveness and Safety of Ambulatory Care Warfarin Management by Non-physician Providers Versus Usual Medical Care: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: Growing evidence suggests that non-physician providers (NPPs) can effectively and safely manage warfarin therapy. This systematic review and meta-analysis aimed to evaluate warfarin management by NPPs compared to usual medical care (UMC) in ambulatory patients. Methods: We conducted a systematic search of PubMed (MEDLINE), Ovid Embase, Ovid International Pharmaceutical Abstracts, Scopus, CINAHL (EBSCO), and the Cochrane Central Register of Controlled Trials (CENTRAL) from inception to January 2024. Studies were included if they were randomized controlled trials or quasi-experimental designs comparing warfarin management across professions. Two independent reviewers performed title and abstract screening, full-text review, data extraction, and risk of bias assessment. Results were pooled using random effects models. Results: Of 19 122 citations identified, 6 met the inclusion criteria. NPPs included pharmacists (4), nurse practitioners (1), and multidisciplinary teams (1). Meta-analysis showed no significant difference in time spent in therapeutic range (TTR) (mean difference [MD] 1.64%; 95% confidence interval [CI]-1.86 to 5.16, I 2 = 0%)) for NPPs vs UMC. There were no differences in thrombosis (relative risk [RR] 1.23; 95% CI 0.36 to 4.23, I 2 = 0%), hemorrhage (RR = 1.07; 95% CI 0.44 to 2.63, I 2 = 0%), mortality (RR = 0.94; 95% CI 0.33 to 2.67, I 2 = 0%), or patient satisfaction (standardized mean difference [SMD] 0.56; 95% CI -0.04 to 1.15, I 2 = 85%). Conclusion: NPP management resulted in similar TTR as UMC. Due to few thromboembolic and hemorrhagic events, more studies are needed to determine the effects of NPP warfarin management on clinical outcomes.
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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 | 0.026 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.038 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".