Current status of haemophilia inhibitor management in mainland China: a haemophilia treatment centres survey on treatment preferences and real‐world clinical practices
Bibliographic record
Abstract
To investigate the current experience and expertise for haemophilia inhibitor patient management in haemophilia treatment centres (HTCs) in mainland China. Questionnaires were distributed to 'tertiary tier A' hospital HTCs across China to collect information on treatment preferences for bleeding control, prophylaxis and inhibitor eradication, as well as their regimens in real-world clinical practice. Of 40 questionnaires distributed, 39 were returned. In all, 38 were analysable for treatment preferences and 34 for actual clinical practice. For haemostatic treatment, 76·3% (29/38) HTCs preferred activated recombinant human Factor VII (rFVIIa). In clinical practice, the most widely used by-pass agent was prothrombin complex concentrate (26 HTCs). Although 65·8% (25/38) of HTCs believed prophylaxis treatment was necessary, it was prescribed in only 12. Similarly, 65·8% (25/38) of HTCs believed immune tolerance induction (ITI) therapy was necessary but only 14·8% (92/622) of patients in 19 HTCs received low-dose ITI treatment. HTCs in relatively economically developed cities (with higher-than-average per-capita gross domestic product) had better access to haemostatic treatment, coagulation testing and were more likely to provide prophylaxis and ITI in practice. The present survey showed there were gaps in haemophilia inhibitor care between the HTC physicians' preferences and their actual clinical practice. More specific care guidelines, education and clinical decision support tools are needed to guide clinical practice.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".