Shared decision-making for the treatment of aortic stenosis (SEEK-AS): rationale and study design of the implementation of a patient decision aid to facilitate a high-quality treatment decision
Bibliographic record
Abstract
Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Heart & Stroke Foundation (Canada) Background Shared decision-making (SDM) is endorsed by multiple guidelines to help clinicians and patients achieve a high-quality treatment decision that integrates patients’ priorities, preferences and values. Patient decision aids (PDAs) are evidence-based tools designed to support the process of SDM. There is a gap in knowledge translation to facilitate the adoption of guideline recommendations for the treatment of aortic stenosis (AS). Rationale: There is strong evidence of improved outcomes associated with SDM; in contrast, implementation research is needed to address the pressing question of "how to do SDM". Therefore, the SEEK-AS study aims to plan and evaluate the implementation of a PDA for AS to facilitate the integration of SDM in the treatment of valvular heart disease. Study Design: We will present the design of a prospective multi-method implementation study of a novel set of paper-based and individualised web-based PDAs for AS. We aim to explore diverse perspectives and uncover barriers and enablers to investigate the "real-world" implementation of the PDAs. The study prioritises patient and family engagement, and is informed by the Ottawa Model for Research Use (OMRU) conceptual model to guide the sequential objectives: (1) select and tailor implementation strategies in four distinct healthcare organisations, across diverse patient and clinician populations to develop distinct implementation blueprints, and (2) evaluate the implementation of the PDAs across health care organisations and users. Data collection will include a series of in-depth focus group and individual interviews with patients treated with different modalities and multidisciplinary clinicians using content analysis organised under the OMRU levels (innovation, potential adopters, and practice environment; objectives 1 and 2), and patient and clinician surveys using outcomes mapped to OMRU levels and patient/clinician levels (objective 2). Anticipated Research Output: We will present the stakeholder engagement plan and patient engagement strategies. The findings of the study will include the identification of barriers and facilitators of regional implementation, and provide evidence to guide the successful adoption of PDAs to help shift the culture of care from clinician-driven to patient-centred and improve the care of patients with valvular heart disease.
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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 | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".