Large scale studies assessing anti‐factor <scp>VIII</scp> antibody development in previously untreated haemophilia A: what has been learned, what to believe and how to learn more
Bibliographic record
Abstract
Minimizing the risk of inhibitor development by acting on modifiable risk factors remains a sensible goal for treatment optimization in haemophilia A. By critically appraising published studies assessing inhibitor development, this review addresses the role of studies in previously untreated patients (PUPs) for establishing the immunogenicity of new concentrates, suggest novel research design to be adopted in future studies and discuss clinical practice implications of the reported differential immunogenicity of Kogenate Bayer and Advate factor VIII concentrates. Three considerations are relevant here: (i) all of the existing concentrates, when tested following the International Society on Thrombosis and Haemostasis Scientific and Standardization Committee recommendation, were shown to be safe; as a consequence, (ii) when considering using any newly introduced product, one should be aware that it could, in future, turn out to be as immunogenic as Kogenate Bayer, and (iii) at the population level, it might be wiser not to use Kogenate Bayer in PUPs, if the choice is against Advate. When presenting the risk of developing inhibitors to the individual patient (or their family), the message remains that the risk can be as high as 40%, without any efficient instrument to predict individual inhibitor risk. Patients should be invited to enrol into a randomized registry trial, including random assignment to trials with new investigational products.
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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 | 0.045 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".