96-72: Gender Differences in Atrial Fibrillation Patient's Knowledge and Perceptions about Oral Anticoagulation Therapy: Results from an International Survey
Bibliographic record
Abstract
Background: Stroke knowledge and perception of stroke risk are likely to affect oral anticoagulant (OAC) treatment preferences in patients with atrial fibrillation (AF). This international prospective study investigated the influence of gender on patient perceptions of AF, stroke knowledge, preferences for oral anticoagulation (OAC) treatment decisions, and attributes of OAC affecting treatment choice. Methods: Cross-sectional survey of 937 AF patients receiving OAC [overall mean (SD) age 54.3(16.6) years; 37.1% female; mean(SD) CHA2 DS2 -VASc score 2.6(1.7)] recruited from 5 countries (USA, Canada, Germany, Japan, France). Results: Overall AF was perceived as a serious condition, with women significantly more likely to perceive AF as extremely serious than men (Table). Men were more likely to report never being concerned about stroke risk compared to women. No gender differences in stroke knowledge and self-reported adherence to OAC. Stroke prevention was the most important factor when choosing OAC for both men and women, followed by risk of major bleeding and other side effects. Dosing frequency was significantly less important than stroke prevention. Conclusion: Women perceived AF as more serious than men, with men more likely to report not being concerned about stroke risk. Both men and women valued stroke prevention as the most important factor when choosing OAC Table *p < 0.05; **p < 0.001 vs. men
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".