Shared Decision-Making Aid for Stroke-Prevention Strategies in Patients With Atrial Fibrillation Receiving Maintenance Hemodialysis (SIMPLIFY-HD): A Mixed-Methods Study
Bibliographic record
Abstract
Background: Recent atrial fibrillation guidelines recommend shared decision-making between clinicians and patients when choosing stroke-prevention therapies. Although decision aids improve patients’ knowledge and decisional conflicts, there is no decision aid for stroke-prevention strategies in people with atrial fibrillation receiving hemodialysis. Objective: The objective was to develop and field test the first decision aid for Atrial Fibrillation in HemoDialysis (AFHD-DA) for stroke prevention in atrial fibrillation and hemodialysis. Design: This is a sequential 3-phase mixed-methods study following the International Patient Decision Aid Standards and the Ottawa Decision Support Framework. Setting: This study was conducted in 2 ambulatory hemodialysis centers in Montreal and Laval (Canada). Participants: Adults with atrial fibrillation receiving hemodialysis and clinicians (physicians, pharmacists, or nurse practitioners) involved in their care. Methods: In phase 1, we conducted systematic and 2 rapid reviews and formed the steering committee to pilot the first version of AFHD-DA. In phase 2, we refined the AFHD-DA through 4 rounds of focus groups and interviews, using a qualitative analysis of transcripts and a descriptive analysis of acceptability and usability scores. In phase 3, we field-tested the decision aid during 16 simulated clinical consultations. We assessed decisional conflict and patient knowledge using before-and-after paired t-tests and compared the proportion of patients with high decisional conflict using McNemar’s test. We used the Ottawa Hospital preparation for decision-making scale and participants’ feedback to evaluate how AFHD-DA facilitated shared decision-making. Results: We enrolled 8 patients and 10 clinicians in phase 2. The predefined usability and acceptability thresholds (68 and 66, respectively) were reached. Theme saturation was achieved in the fourth round of focus groups and interviews. Four major themes emerged: acceptability, usability, decision-making process, and scientific value of the decision aid. Sixteen patients and 10 clinicians field-tested the decision aid in phase 3. In clinical settings, AFHD-DA significantly decreased the mean decisional conflict score from 41.0 to 13.6 ( P < .001) and the proportion of patients with decisional conflicts from 81.3 to 18.8% ( P = .002). It improved the patients’ mean knowledge score from 62.7 to 76.6 ( P = .001), and 81% of patients and 90% of clinicians felt highly prepared for decision-making. Clinical consultations lasted, on average, 21 minutes (standard deviation = 8). Limitations: The main limitations were the low quality of existing literature, the small number of participants, and the absence of a control group. Conclusions: The decision aid facilitated time-efficient shared decision-making between clinicians and patients, improved patients’ knowledge, and reduced decisional conflict around selecting a stroke-prevention strategy for patients with atrial fibrillation receiving hemodialysis.
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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 | 0.048 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".