The relative importance of barriers to the prescription of warfarin for nonvalvular atrial fibrillation.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Despite the publication of a number of randomized, controlled trials demonstrating a substantial reduction in stroke with anticoagulation in patients with nonvalvular atrial fibrillation, the 'real world' use of warfarin is sub-optimal. Previous surveys have attempted to explain this problem but have significant limitations. The purpose of this study was to assess the relative importance of various barriers that may influence the prescription of warfarin in patients with nonvalvular atrial fibrillation. METHODS: This cross-sectional survey was mailed to all practising cardiologists, neurologists and internists, as well as a random sample of family physicians within Alberta. Physicians caring for patients with NVAF rated the relative importance of potential barriers using a Likert scale. RESULTS: Sixty-seven per cent of all physicians returned the survey. Overall, barriers pertaining to the patient's clinical characteristics were rated to be more important than those pertaining to the physician or to the organization required when prescribing these therapies. Specifically, an ongoing history of falls, a history of bleeding within the previous year and an inability to comply with therapy were rated as important barriers by 64%, 55% and 53% of physicians, respectively. Most physicians strongly believed that patients should receive information on the benefits and risks of warfarin (96%) and that patients should have a say in whether warfarin is prescribed (86%). IMPLICATIONS: This study suggests that most of the barriers to warfarin use pertain to patient clinical characteristics and the need for patients to be involved in the decision to initiate therapy. The use of decision support technologies would facilitate involvement of the patient and serve to educate both the patient and physician on the risks and benefits of warfarin therapy.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".