Factors Associated With Patient Engagement in Shared Decision-Making for Stroke Prevention Among Older Adults with Atrial Fibrillation
Bibliographic record
Abstract
OBJECTIVE: To examine the extent of, and factors associated with, patient engagement in shared decision-making (SDM) for stroke prevention among patients with atrial fibrillation (AF). METHODS: We used data from the Systematic Assessment of Geriatric Elements-Atrial Fibrillation study which includes older ( ≥65 years) patients with AF and a CHA2DS2-VASc≥2. Participants reported engagement in SDM by answering whether they actively participated in choosing to take an oral anticoagulant (OAC) for their condition. Multiple logistic regression was used to assess associations between sociodemographic, clinical, geriatric, and psychosocial factors and patient engagement in SDM. RESULTS: A total of 807 participants (mean age 75 years; 48% female) on an OAC were studied. Of these, 61% engaged in SDM. Older participants (≥80 years) and those cognitively impaired were less likely to engage in SDM, while those very knowledgeable of their AF associated stroke risk were more likely to do so than respective comparison groups. CONCLUSIONS: A considerable proportion of older adults with AF did not engage in SDM for stroke prevention with older patients and those cognitively impaired less likely to do so. Clinicians should identify patients who are less likely to engage in SDM, promote patient engagement, and foster better patient-provider communication which may enhance long-term patient outcomes.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".