A path forward for the implementation of shared decision-making in valvular heart disease: global joint recommendations from clinicians, patients and researchers
Bibliographic record
Abstract
BACKGROUND: Shared decision-making (SDM) is widely endorsed in international guidelines for the treatment of valvular heart disease (VHD). Despite evidence that the process improves outcomes and does not increase the burden of consultations, SDM has not been adopted as a standard of care across regions and diverse health systems. METHODS: We conducted a 3-phase study co-led by clinicians and people with lived experience using an integrated knowledge translation approach guided by the knowledge-to-action framework. In a preparatory phase, we conducted exploratory semi-structured interviews with 19 international and diverse experts to identify barriers and enablers to SDM in VHD; we used thematic analysis to identify the major issues to inform project development. We convened an in-person meeting of patients and patient advocates (n = 9), clinicians (n = 11) and researchers (n = 3) from 10 countries to build joint recommendations. Lastly, we conducted a series of local and international meetings to validate the findings and inform future initiatives. RESULTS: Challenges identified included (1) concerns about clinicians' availability and time requirements, (2) uncertainty about how to practice SDM and (3) absence of regional data to evaluate SDM in VHD. The joint recommendations clustered on five global areas of focus and six sets of recommendations tailored to regional contexts and cultural norms. Final recommendations on (1) preparing patients and carers, (2) training healthcare teams and (3) creating a supportive system were further enhanced by VHD knowledge users' input in various regional settings. CONCLUSIONS: This first report co-led by diverse stakeholders offers a practice and policy-ready roadmap to strengthen the implementation and evaluation of SDM in VHD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".