Evaluation of patients’ attitudes towards stroke prevention and bleeding risk in atrial fibrillation
Bibliographic record
Abstract
Patient's values and preferences regarding the relative importance of preventing strokes and avoiding bleeding are now recognised to be of great importance in deciding on therapy for the prevention of stroke due to atrial fibrillation (SPAF). We used an iPad questionnaire to determine the minimal clinically important difference (Treatment Threshold) and the maximum number of major bleeding events that a patient would be willing to endure in order to prevent one stroke (Bleeding Ratio) for the initiation of antithrombotic therapy in 172 hospital in-patients with documented non-valvular atrial fibrillation in whom anticoagulant therapy was being considered. Patients expressed strong opinions regarding SPAF. We found that 12% of patients were "medication averse" and were not willing to consider antithrombotic therapy; even if it was 100% effective in preventing strokes. Of those patients who were willing to consider antithrombotic therapy, 42% were identified as "risk averse" and 15% were "risk tolerant". Patients required at least a 0.8% (NNT=125) annual absolute risk reduction and 15% relative risk reduction in the risk of stroke in order to agree to initiate antithrombotic therapy, and patients were willing to endure 4.4 major bleeds in order to prevent one stroke. In conclusion, there was a substantial amount of inter-patient variability, and often extreme differences in opinion regarding tolerance of bleeding risk in the context of stroke prevention in atrial fibrillation. These findings highlight the importance of considering patient preferences when deciding on SPAF 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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".