Biological considerations of plasma-derived and recombinant factor VIII immunogenicity
Bibliographic record
Abstract
In hemophilia A, the most severe complication of factor VIII (FVIII) replacement therapy involves the formation of FVIII neutralizing antibodies, also known as inhibitors, in 25% to 30% of patients. This adverse event is associated with a significant increase in morbidity and economic burden, thus highlighting the need to identify methods to limit FVIII immunogenicity. Inhibitor development is regulated by a complex balance of genetic factors, such as FVIII genotype, and environmental variables, such as coexistent inflammation. One of the hypothesized risk factors of inhibitor development is the source of the FVIII concentrate, which could be either recombinant or plasma derived. Differential immunogenicity of these concentrates has been documented in several recent epidemiologic studies, thus generating significant debate within the hemophilia treatment community. To date, these discussions have been unable to reach a consensus regarding how these outcomes might be integrated into enhancing clinical care. Moreover, the biological mechanistic explanations for the observed differences are poorly understood. In this article, we complement the existing epidemiologic investigations with an overview of the range of possible biochemical and immunologic mechanisms that may contribute to the different immune outcomes observed with plasma-derived and recombinant FVIII 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".