P3618Medication burden and perception of anticoagulation treatments in atrial fibrillation patients: results from an international survey
Bibliographic record
Abstract
Background: Multiple daily medications and frequent dosing regimens may be burdensome to atrial fibrillation (AF) patients. Purpose: To assess how medication burden influences patient perceptions of, preferences for and self-perceived adherence to oral anticoagulants (OAC). Methods: International, cross-sectional, prospective survey of 929 AF patients taking OAC for stroke prevention conducted in the USA, Canada, France, Germany and Japan. Results: Patients (mean age 54.3±16.6 years, mean CHA2DS2-VASc 2.6±1.7) took a median of 3 (IQR 2–6) different medications per day. Patients taking ≥4 medications per day were significantly older, with a higher mean CHA2DS2-VASc score than those taking <4. Patients with recent stroke (n=190) took significantly fewer daily medications (median 3 [IQR 2–4]) vs. those without a recent stroke (n=739; median 4 [IQR 2–6]), p<0.001. Independent of the number of daily medications or dosing frequency (once daily or more frequently), stroke prevention was rated as the most important OAC attribute (Table). Overall, 44.9% of patients showed a strong preference for taking more pills to reduce stroke risk. This proportion was significantly greater (54.2%) in those on ≥4 medications per day (p<0.001 vs. other groups pooled). Only 8.1% rated dosing frequency as the most important attribute of an OAC, independent of daily medication burden or current dosing frequency. Of note, 87.5% of patients stated that taking medications was not a burden or was a manageable inconvenience. Overall, 79.9% of patients reported always taking their OAC as prescribed; the proportion was highest among those on ≥4 medications per day (84.5%, p<0.001 vs. other groups pooled).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".