Stakeholder perspectives on clinical research related to therapies for rare diseases: therapeutic misconception and the value of research
Bibliographic record
Abstract
BACKGROUND: For many rare diseases, few treatments are supported by strong evidence. Patients, family members, health care providers, and policy-makers thus have to consider whether to accept, recommend, or fund treatments with uncertain clinical effectiveness. They must also consider whether and how to contribute to clinical research that may involve receiving or providing the therapy being evaluated. OBJECTIVE: To understand why and how patients and families with rare metabolic diseases, specialist metabolic physicians, and health policy advisors choose whether to participate in studies and how they use and value research. METHODS: We conducted separate focus group interviews with each stakeholder group (three groups in total); two groups were conducted by telephone and the third was held in-person. Participants were recruited using purposive sampling. We analyzed each interview transcript sequentially using a qualitative description approach to inductively identify key themes. Several strategies to ensure credibility and trustworthiness were used including debriefing sessions after each focus group and having multiple team members review transcripts. RESULTS: Four patients/caregivers, six physicians, and three policy advisors participated. Our findings did not support conventional perspectives that therapeutic misconception (gaining access to treatment) is the main motivating factor for patients/caregivers to participate in clinical research. Rather, patients'/caregivers' expressed reasons for participating in research included advancing science for the next generation and having an opportunity to share their experiences. Patients/caregivers and physicians described the difficulties in weighing risks versus benefits of accepting treatments not well-supported by evidence. Physicians also reported feeling conflicted in their dual role as patient advisor/advocate and evaluator of the evidence. Policy advisors were primarily focused on critically appraising the evidence to make recommendations for the health system. CONCLUSIONS: Stakeholders differ in their perspectives on rare disease research but share concerns about the risks versus benefits of therapies when making individual- and population-level decisions.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| 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".