Patients with haemophilia A with inhibitors in China: a national real‐world analysis and follow‐up
Bibliographic record
Abstract
The development of alloantibodies (inhibitors) against coagulation factor VIII (FVIII) is the most serious complication of FVIII replacement therapy in patients with haemophilia A (HA). We carried out a nationwide study focussing on patients with HA with inhibitors in China to evaluate the condition and management of this population. The study retrospectively analysed patient characteristics, clinical history, manifestation, treatment strategy as well as individual haemophilia care of 493 patients with inhibitors (466 with severe HA and 27 with non-severe HA) registered all over China. The median (interquartile range) age at diagnosis of FVIII inhibitors was 13 (5-28) years in patients with severe HA and 24 (10·5-39·5) years in patients with non-severe HA. Most patients (85%) had high-titre inhibitors. Prothrombin complex concentrate and recombinant activated coagulation factor VII were used respectively in 76·2% and 29·2% of patients for acute bleeding. Only 22·3% of patients underwent immune tolerance induction (ITI) treatment, of whom 64·9% achieved negative inhibitor titre. In patients who did not undergo ITI, the inhibitors turned negative in 17·7%, and patients with low peak inhibitor titre were more likely to acquire negative titre spontaneously (odds ratio 11·524, 95% confidence interval 5·222-25·432; P = 0·000). We recorded that 3·2% of the patients died from haemophilia-related life-threatening bleeding.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".