Values and Preferences of Physicians and Patients With Nonvalvular Atrial Fibrillation Who Receive Oral Anticoagulation Therapy for Stroke Prevention
Bibliographic record
Abstract
BACKGROUND: Real-world data on patients' and physicians' values related to the use of oral anticoagulant (OAC) therapy for stroke prevention in patients with nonvalvular atrial fibrillation are currently lacking. We sought to assess the values, preferences, and experience of patients who receive OAC therapy, and of physicians who prescribe OAC therapy. METHODS: A national survey of randomly selected patients (n = 266) and physicians (n = 178) was conducted between May and September 2014. Each was asked to evaluate the importance of individual OAC attributes and identify which of 2 medication profiles they would prefer (individual attributes were progressively modified to determine which were the most valued and/or influenced treatment choice). Medication adherence and prescription practice was also assessed. RESULTS: The preferences of patients and physicians regarding OAC therapy differed but largely focused on characteristics related to safety and, to a lesser extent, efficacy. When based solely on the basis of the attribute profile (blinded to the specific agent), physicians were more likely to select apixaban (61%), whereas patients showed no significant preference among apixaban, rivaroxaban, and warfarin. Despite this, 49% of physicians spontaneously stated rivaroxaban as their preferred agent (vs 25% apixaban). Patients prescribed and taking once daily medications (rivaroxaban or warfarin) showed better compliance with their OAC therapy (approximately 30% of twice daily medications being taken once daily, with significantly more missed doses compared with once daily medications). CONCLUSIONS: Real-world prescriptions do not reflect reported values, which suggests that other factors influence patient-physician decision-making around OAC therapy. Data on self-reported adherence to OAC therapy and discordance in the use of OACs from prescribed regimens are concerning and warrant further investigation.
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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 | 0.000 |
| 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".