“What Is the Right Decision for Me?” Integrating Patient Perspectives Through Shared Decision-Making for Valvular Heart Disease Therapy
Bibliographic record
Abstract
Innovations in the treatment of valvular heart disease have transformed treatment options for people with valvular heart disease. In this rapidly evolving environment, the integration of patients' perspectives is essential to close the potential gap between what can be done and what patients want. Shared decision-making (SDM) and the measurement of patient-reported outcomes (PROs) are two strategies that are in keeping with this aim and gaining significant momentum in clinical practice, research, and health policy. SDM is a process that involves an individualised, intentional, and bidirectional exchange among patients, family, and health care providers that integrates patients' preferences, values, and priorities to reach a high-quality consensus treatment decision. SDM is widely endorsed by international valvular heart disease guidelines and increasingly integrated in health policy. Patient decision aids are evidence-based tools that facilitate SDM. The measurement of PROs-an umbrella term that refers to the standardised reporting of symptoms, health status, and other domains of health-related quality of life-provides unique data that come directly from patients to inform clinical practice and augment the reporting of quality of care. Sensitive and validated instruments are available to capture generic, dimensional, and disease-specific PROs in patients with valvular heart disease. The integration of PROs in clinical care presents significant opportunities to help guide treatment decision and monitor health status. The integration of patients' perspectives promotes the shift to patient-centred care and optimal outcomes, and contributes to transforming the way we care for patients with valvular heart disease.
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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 | 0.067 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.006 | 0.021 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".