Systematic Review of Patient Decision Aids for Stroke Prevention Therapy in Atrial Fibrillation Management
Bibliographic record
Abstract
Background: Atrial Fibrillation (AF) is a major cause of stroke. Oral anticoagulation can reduce the risk of AF-associated stroke by 65% but it remains underused. Stroke prevention therapy in patients with AF has been considered a good target for shared decision making with patient decision aids as it is a long-term, preference-sensitive decision with known risk-benefit trade-offs. The aim of this systematic review was to summarize published literature on the effectiveness of patient decision aids on the choice of and adherence to stroke prevention therapy in individuals with AF. Methods: We conducted a structured literature search for prospective studies evaluating decision aids for AF stroke prevention therapy in adult patients with nonvalvular AF. We included studies that compared those exposed to a decision aid with a control condition for outcomes including choice of therapy, adherence, decisional conflict and patient knowledge. Quantitative meta-analysis was not feasible due to excessive between-study heterogeneity. Results: Eight studies met inclusion and exclusion criteria. Six studies were randomized clinical trials and two were pre-post comparisons. Of the 8 studies, each evaluated a different decision aid, with only three including all contemporary oral anticoagulant drugs. All decision aids improved AF knowledge compared to baseline or control and decision aids reduced decisional conflict in four of six studies. However, there were inconsistent effects of the studied decision aids on initiation of oral anticoagulation. Adherence to initial stroke prevention therapy choice appeared to benefit from decision aid use in 2 studies that addressed this issue. Conclusions: Decision aids for stroke prevention increased AF patients' knowledge and decisional confidence but had variable impacts on choice of and adherence to stroke prevention therapy. The results highlight the need for well-designed decision aids that present patients with all contemporary therapeutic options.
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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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".