Impact of stated barriers on proposed warfarin prescription for atrial fibrillation: a survey of Canadian physicians
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) is a common cardiac arrhythmia, and leading cause of ischemic stroke. Despite proven effectiveness, warfarin remains an under-used treatment in atrial fibrillation patients. We sought to study, across three physician specialties, a range of factors that have been argued to have a disproportionate effect on treatment decisions. METHODS: Cross-sectional survey of Canadian Family Doctors (FD: n = 500), Geriatricians (G: n = 149), and Internal Medicine specialists (IMS: n = 500). Of these, 1032 physicians were contactable, and 335 completed and usable responses were received. Survey questions and clinical vignettes asked about the frequency with which they see patients with atrial fibrillation, treatment practices, and barriers to the prescription of anticoagulants. RESULTS: Stated prescribing practices did not significantly differ between physician groups. Falls risk, bleeding risk and poor patient adherence were all highly cited barriers to prescribing warfarin. Fewer geriatricians indicated that history of patient falls would be a reason for not treating with warfarin (G: 47%; FD: 71%; IMS: 72%), and significantly fewer changed reported practice in the presence of falls risk (χ (2) (6) = 45.446, p < 0.01). Experience of a patient having a stroke whilst not on warfarin had a significant impact on vignette decisions; physicians who had had patients who experienced a stroke were more likely to prescribe warfarin (χ (2) (3) =10.7, p = 0.013). CONCLUSIONS: Barriers to treatment of atrial fibrillation with warfarin affect physician specialties to different extents. Prior experience of a patient suffering a stroke when not prescribed warfarin is positively associated with intention to prescribe warfarin, even in the presence of falls risk.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".