Evaluating the Effect of a Patient Decision Aid for Atrial Fibrillation Stroke Prevention Therapy
Bibliographic record
Abstract
BACKGROUND: Stroke prevention therapy decisions for patients with atrial fibrillation (AF) are complex and require trade-offs, but few validated patient decision aids (PDAs) are available to facilitate shared decision making. OBJECTIVE: To evaluate the effects of a novel PDA on decision-making parameters for AF patients choosing stroke prevention therapy. METHODS: We developed an evidence-based individualized online AF PDA for stroke prevention therapy and evaluated it in a prospective observational pilot study. The primary outcome was decisional conflict. Secondary outcomes were knowledge, usability/acceptability, patient preferences, effects on therapy choices, and participant feedback. RESULTS: 37 participants completed the PDA. The PDA could be completed independently and was well accepted. It significantly decreased the mean decisional conflict score ( P < 0.001) and all its subscales and increased participant AF knowledge ( P = 0.02). 76% of participants indicated that their individualized therapy attribute ranking was congruent with their values. The PDA-generated best-match therapy was chosen by 70% of participants in decision 1 (no therapy, aspirin, or oral anticoagulant), and 17% for decision 2 (choice of anticoagulant). Among AF patients, 60% chose a different drug than that currently prescribed to them. Conclusion and Relevance: Our PDA was effective for reducing decisional conflict, increasing patient knowledge, eliciting patients' values, and presenting therapy options that aligned with patients' values and preferences. Using the PDA revealed that many patients have therapy preferences different from their currently prescribed treatment. The PDA is a practical and potentially valuable tool to facilitate decision making about stroke prevention therapy for AF.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".