Inhibitors in nonsevere haemophilia A: outcome and eradication strategies
Bibliographic record
Abstract
In nonsevere haemophilia A (HA) patients the presence of an inhibitor may exacerbate the bleeding phenotype dramatically. There are very limited data on the optimal therapeutic approach to eradicate inhibitors in these patients. We aimed to describe inhibitor eradication treatment in a large cohort of unselected nonsevere HA patients with inhibitors. We included 101 inhibitor patients from a source population of 2,709 nonsevere HA patients (factor VIII 2-40 IU/dl), treated in Europe and Australia (median age 37 years, interquartile range (IQR) 15-60; median peak titre 7 BU/ml, IQR 2-30). In the majority of the patients (71 %; 72/101) the inhibitor disappeared; either spontaneously (70 %, 51/73) or after eradication treatment (75 %, 21/28). Eradication treatment strategies varied widely, including both immune tolerance induction and immunosuppression. Sustained success (no inhibitor after rechallenge with factor VIII concentrate after inhibitor disappearance) was achieved in 64 % (30/47) of those patients rechallenged with FVIII concentrate. In high-titre inhibitor patients sustained success was associated with eradication treatment (unadjusted relative risk 2.3, 95 % confidence interval 1.3-4.3), compared to no eradication treatment. In conclusion, in nonsevere HA patients most inhibitors disappear spontaneously. However, in 35 % (25/72) of these patients an anamnestic response still can occur when rechallenged, thus disappearance in these patients does not always equal sustained response. Treatment for those requiring eradication has to be decided case by case, as one single approach is unlikely to be appropriate for all.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".