The demographics, treatment characteristics and quality of life of adult people with haemophilia in China – results from the <scp>HERO</scp> study
Bibliographic record
Abstract
INTRODUCTION: Haemophilia management in China needs to be further developed. To further improve the quality of life (QoL) of people with haemophilia (PWH) in China, it is important to investigate the peculiarities of China as compared to other countries. AIM: The primary objective of the Haemophilia Experiences, Results and Opportunities (HERO) project was to quantify the impact of key psychosocial factors affecting PWH. This article presents the demographics, treatment characteristics, and QoL of adult PWH in China as compared with the results of the other nine countries participating in the HERO study. METHODS: This was a web- (except in Algeria) and questionnaire-based survey conducted in 10 countries. RESULTS: A total of 110 adult PWH from China and 565 from other countries completed the questionnaire. Compared with other countries, respondents in China reported: lower rate of employment (45.6% vs. 63.1%); lower percentages of being treated by prophylaxis (4.1% vs. 36.8%), being treated always at home (27.8% vs. 54.3%) and following treatment recommendation as instructed (6.2% vs. 40.5%); greater difficulty in obtaining replacement factor products (97.3% vs. 29.6%) and visiting their treatment centre (60.9% vs. 26.4%); more annual bleeds requiring treatment (mean: 29.4/year vs. 15.4/year); lower mean self-evaluated disease control score (5.5 vs. 7.7), EQ-5D index (0.71 vs. 0.75) and visual analogue scale (7.1 vs. 7.5) scores. Employed PWH in China had a better self-reported generic QoL than those unemployed. CONCLUSIONS: The study suggests that there is a major need for further improvement of both medical care and ongoing psychosocial support for PWH in China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".