Exploring Patient Viewpoints to Optimize Implementation of a Biological Therapy for Atrial Fibrillation Prevention
Bibliographic record
Abstract
Background: Embracing patient viewpoints can enhance the translation of novel therapeutics to clinical settings. This study evaluated the acceptability of using extracellular vesicles (EVs) as a biological therapy for preventing postoperative atrial fibrillation (AF), through engagement with patients, providing insights into their attitudes and information needs. Methods: Patients participated in prerecorded presentations, virtual focus groups, and surveys to assess their perspectives on EV therapy and determine the factors influencing their acceptance of the intervention. Results: Participants with postoperative AF experienced prolonged intensive care unit and hospital stays, compared to those of patients with normal heart rhythm. Prior to the presentation, a number of participants were unfamiliar with postoperative AF and biological therapies. However, postpresentation and post-focus group activities resulted in enhanced understanding of the research, with high levels of comprehension reported by all participants. The level of acceptance of EV therapy tended to increase, with a majority expressing willingness to participate in clinical trials and accept the therapy. The focus groups identified and addressed common questions regarding the potential risks and side effects of EVs, their source, dosing, utility for patients with preexisting AF, and the risk of human immunodeficiency virus (HIV) contraction or allergic reactions. Conclusions: The study highlights the importance of providing education, involving the patient's circle of care, and addressing patient concerns, to promote acceptance of therapies such as EV therapy for postoperative AF. Clinical Trial Registration: NCT05032495.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".