Patient preferences and priorities for haemophilia gene therapy in the US: A discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: Gene therapy has shown promise in clinical trials for patients with haemophilia, but patient preference studies have focused on factor replacement treatments. AIM: We conducted a discrete choice experiment (DCE) to investigate the relative importance and differential preferences patients provide for gene therapy attributes. METHODS: We surveyed male adults with haemophilia in the United States recruited from patient panels including the National Hemophilia Foundation Community Voices in Research platform using an online survey over 4 months in 2020/21. Participants indicated preferences for gene therapy attributes including dosing frequency/durability, effect on annual bleeding, uncertainty related to side effects, impact on daily activities, impact on mental health, and post-treatment requirements. The relative importance of each attribute was analysed overall and for subgroups based on haemophilia type and severity. RESULTS: A total of 183 males with haemophilia A (n = 120) or B (n = 63) were included. Half (47%) had severe haemophilia; most (75%) were White. Overall, participants gave effect on bleeding rate the greatest relative importance (31%), followed by dose frequency/durability (26%), uncertainty regarding safety issues (17%), and impact on daily activities (11%). Dose frequency/durability had the greatest importance for those with haemophilia B (35%). CONCLUSION: People with haemophilia prioritised reduced bleeding and treatment burden; the former was more important in haemophilia A and the latter in haemophilia B, followed by safety and impact on daily life in this DCE of gene therapy attributes. These findings and differences can inform clinical and health policy decisions to improve health equity for people with haemophilia.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".