A pilot process to identify and switch high-risk warfarin patients to direct oral anticoagulants
Bibliographic record
Abstract
Objective: To pilot a process for identifying and switching high-risk warfarin patients to direct oral anticoagulants (DOACs) in nonvalvular atrial fibrillation (AF) or venous thromboembolism (VTE).Methods: A pharmacy resident identified high-risk warfarin patients using three criteria. The resident reviewed medical charts, communicated recommendations to the primary care physician (PCP), and scheduled patients for appointments to switch to DOACs. Patients were followed every 1 to 3 months after initiating DOACs. The primary outcome of the study was the percentage of high-risk warfarin patients before and after process implementation. Secondary outcomes at 6 months included bleeding or thrombotic events, patient-reported side effects, recommendation acceptance rates, adherence rates, clinic time in therapeutic range (TTR), and patient satisfaction.Results: Out of 76 patients evaluated, 22.4% were identified as high-risk using the pre-specified criteria. After program implementation, this percentage was reduced to 9.2%. No significant adverse events occurred by 3-month follow-up, and 100% of recommendations were accepted by physicians. Adherence rates and clinic TTR also improved after implementation (87.5% to 94% and 60.9% to 81.1%, respectively). Overall, patients reported satisfaction with switching from warfarin to DOAC.Conclusions: A pilot process was successful in reducing the percentage of high-risk warfarin patients by switching to DOAC therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".