Preference-Based Antithrombotic Therapy in Atrial Fibrillation: Implications for Clinical Decision Making
Bibliographic record
Abstract
BACKGROUND: Patient preferences and expert-generated clinical practice guidelines regarding treatment decisions may not be identical. The authors compared the thresholds for antithrombotic treatment from studies that determined or modeled the treatment preferences of patients with atrial fibrillation with recommendations from clinical practice guidelines. METHODS: Methods included MEDLINE identification, systematic review, and pooling with some reanalysis of primary data from relevant studies. RESULTS: Eight pertinent studies, including 890 patients, were identified. These studies used 3 methods (decision analysis, probability tradeoff, and decision aids) to determine or model patient preferences. All methods highlighted that the threshold above which warfarin was preferred over aspirin was highly variable. In 6 of 8 studies, patient preferences indicated that fewer patients would take warfarin compared to the recommendations of the guidelines. In general, at a stroke rate of 1% with aspirin, half of the participants would prefer warfarin, and at a rate of 2% with aspirin, two thirds would prefer warfarin. In 3 studies, warfarin must provide at least a 0.9% to 3.0% per year absolute reduction in stroke risk for patients to be willing to take it, corresponding to a stroke rate of 2% to 6% on aspirin. CONCLUSIONS: For patients with atrial fibrillation, treatment recommendations from clinical practice guidelines often differ from patient preferences, with substantial heterogeneity in their individual preferences. Since patient preferences can have a substantial impact on the clinical decision-making process, acknowledgment of their importance should be incorporated into clinical practice guidelines. Practicing physicians need to balance the patient preferences with the treatment recommendations from clinical practice guidelines.
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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 | 0.169 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".