Societal perspectives on disease and treatment attributes characterizing rare diseases: a qualitative study from the United States
Bibliographic record
Abstract
PURPOSE: Under a societal perspective, disease and treatment attributes that the general public deem important should be considered within value frameworks. The objective was to investigate how members of the general public value attributes beyond health gains and healthcare system expenditures; and better understand their perspectives regarding the importance of attributes typically characterizing rare genetic diseases like Duchenne muscular dystrophy (DMD). METHODS: Qualitative interviews were conducted to elicit feedback on the importance of disease and treatment attributes from general public participants from three US cities. Participants ranked attributes (scale, 1-10) in terms of importance for future research, reported their rationale for ranking, and provided feedback specific to rare diseases. Interview transcripts were coded using NVivo for thematic analysis. RESULTS: The 33 participants (median age, 51 years; 48.5% male) ranked disease severity (mean [median] ranking, 8.7 [9.0]), treatment availability (8.7 [9.0]), and impact on life expectancy (8.4 [9.0]), as most important. The impact on the family, need for equity, and intrinsic value of life were frequently provided rationales. While rare disease as an attribute received a relatively low ranking (6.1 [7.0]), 88% of participants prioritized disease profiles including attributes of severity, health related quality of life (HRQoL) impact, limited lifespan and young age at onset. CONCLUSION: Attributes including disease severity, impact on life expectancy and HRQoL, and treatment availability were all highly important to members of the general public. These findings support the growing evidence regarding the importance of expanding value assessments to include attributes considered important from a societal perspective.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".