Quantifying Benefit-Risk Trade-Offs Toward Prophylactic Treatment Among Adult Patients With Hemophilia A in China: Discrete Choice Experiment Study
Bibliographic record
Abstract
BACKGROUND: Hemophilia A is a chronic condition that requires meticulous treatment and management. Patient preferences for prophylactic treatment can substantially influence adherence, outcomes, and quality of life, yet these preferences remain underexplored, particularly in China. OBJECTIVE: This study aimed to investigate the preferences for prophylactic treatment among Chinese adult patients with hemophilia A without inhibitors, considering clinical effectiveness, side effects, dosing mode, and dosing frequency. METHODS: A discrete choice experiment was used to elicit patient preferences for prophylactic treatment of hemophilia. The study was conducted across 7 provinces in China with socioeconomic and geographical diversity. Subgroup analysis was performed according to education level, geographic location, and treatment type, alongside the exploration of benefit-risk trade-offs. RESULTS: A total of 113 patients completed the discrete choice experiment questionnaire, and we included 102 responses for analysis based on predetermined exclusion criteria. The study found that patients prioritized reducing annual bleeding times and avoiding the risk of developing inhibitors over treatment process attributes. Subgroup analysis revealed that lower-educated patients and those from rural areas attached more importance to the dosing mode, likely due to barriers to self-administration. Patients demonstrated a clear understanding of benefit-risk trade-offs, exhibiting a willingness to accept an increased risk of developing inhibitors for improved clinical outcomes. CONCLUSIONS: This study provides valuable insights into the preferences of patients with hemophilia A for prophylactic treatment in China. Understanding these preferences can enhance shared decision-making between patients and clinicians, fostering personalized prophylactic treatment plans that may optimize adherence and improve clinical outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".