Patient values and preferences for antithrombotic therapy in atrial fibrillation
Bibliographic record
Abstract
Guidelines recommend that patients' values and preferences should be considered when selecting stroke prevention therapy for atrial fibrillation (SPAF). However, doing so is difficult, and tools to assist clinicians are sparse. We performed a narrative systematic review to provide clinicians with insights into the values and preferences of AF patients for SPAF antithrombotic therapy. Narrative systematic review of published literature from database inception. RESEARCH QUESTIONS: 1) What are patients' AF and SPAF therapy values and preferences? 2) How are SPAF therapy values and preferences affected by patient factors? 3) How does conveying risk information affect SPAF therapy preferences? and 4) What is known about patient values and preferences regarding novel oral anticoagulants (NOACs) for SPAF? Twenty-five studies were included. Overall study quality was moderate. Severe stroke was associated with the greatest disutility among AF outcomes and most patients value the stroke prevention efficacy of therapy more than other attributes. Utilities, values, and preferences about other outcomes and attributes of therapy are heterogeneous and unpredictable. Patients' therapy preferences usually align with their values when individualised risk information is presented, although divergence from this is common. Patients value the attributes of NOACs but frequently do not prefer NOACs over warfarin when all therapy-related attributes are considered. In conclusion, patients' values and preferences for SPAF antithrombotic therapy are heterogeneous and there is no substitute for directly clarifying patients' individual values and preferences. Research using choice modelling and tools to help clinicians and patients clarify their SPAF therapy values and preferences are needed.
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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 | 0.025 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".