The evolving role of patient preference studies in health-care decision-making, from clinical drug development to clinical care management
Bibliographic record
Abstract
Introduction: There is a growing trend of using patient preference studies to help incorporate the patient perspective into clinical drug development, care management, and health-care decision-making. Collecting and interpreting patient preference data is integral to multi-stakeholder engagement, patient-centric drug development, and clinical care management. Operationally, challenges exist in understanding ‘when’ and ‘how’ to embark on patient preference studies. This review will provide a brief overview of stated-preference methods, discuss applications throughout the clinical drug development and care management, and highlight how preference studies serve as a powerful tool for quantifying patient experiences for better outcomes.Areas covered: We present case studies to complement the different applications of stated-preference methods in clinical drug development and care management. We discuss the applications of preference data to help inform evidence-based patient advocacy, clinical development strategy, operational feasibility, regulator benefit-risk assessments, health technology assessments, and clinical decision-making.Expert commentary: Patient preference studies can serve as a powerful tool to engage patients and their communities as well as quantify the patient voice across different stages of clinical drug development and care management to support patient-centric health-care decision-making. It is expected that the application of these strategies will quickly advance in the coming years.
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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 | 0.060 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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; both teacher heads agree on what is shown here.
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".