Impact of a Pharmacist‐Led Warfarin Self‐Management Program on Quality of Life and Anticoagulation Control: A Randomized Trial
Bibliographic record
Abstract
STUDY OBJECTIVE: To evaluate the impact of a pharmacist-led warfarin patient self-management program on quality of life and anticoagulation control compared with management in a physician-led specialized anticoagulation clinic. DESIGN: Prospective, randomized, controlled, open-label trial. SETTING: Tertiary care academic medical center. PATIENTS: A total of 114 patients aged 18-75 years who were followed at a specialized anticoagulation clinic, had received warfarin for at least 6 months, and were expected to continue warfarin for a minimum of 4 months. INTERVENTION: All patients attended an educational session on anticoagulation provided by a pharmacist. Patients randomized to the self-management group (58 patients) also received practical training to use the CoaguChek XS device and a self-management dosing algorithm. Patients in the control group (56 patients) continued to undergo standard management at the anticoagulation clinic. MEASUREMENTS AND MAIN RESULTS: Patients completed a validated quality-of-life questionnaire and the validated Oral Anticoagulation Knowledge test at the beginning and end of the study. The quality of anticoagulation control was evaluated by using the time spent in therapeutic range. After 4 months of follow-up, a significant improvement in the self-management group was observed compared with the control group in four of the five quality-of-life topics (p<0.05). Improvements in knowledge were observed in both groups after the training session and persisted after 4 months (p<0.05 for all). The time spent in the therapeutic range (80.0% in the self-management group vs 75% in the control group, p=0.79) and in the extended therapeutic range ([target international normalized ratio ± 0.3] 93.2% in the self-management group vs 91.1% in the control group, p=0.30) were similar between groups. CONCLUSION: A self-management warfarin program led by pharmacists resulted in significant improvement in the quality of life of patients receiving warfarin therapy as well as a reduction in the time required for anticoagulation monitoring, while maintaining a level of anticoagulation control similar to a high-quality specialized anticoagulation clinic.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".