The role of previously untreated patient studies in understanding the development of <scp>FVIII</scp> inhibitors
Bibliographic record
Abstract
Development of inhibitors against factor VIII (FVIII), the major complication of haemophilia A treatment today, is influenced by multiple factors. Genetic (F8 mutation, family history, ethnicity, polymorphisms in immune modulating genes) and non-genetic (intensive exposure to FVIII, presence of pro-inflammatory signals as might occur with large bleeds, infections, surgery, or other immune stimulants [e.g. vaccines]) risk factors as well as their complex inter-relationships contribute to the inhibitor risk profile of haemophilia patients, particularly in the previously untreated patient (PUP) population. Studies in PUPs have been fundamental to furthering the understanding of FVIII inhibitor development, as well as discovering previously unappreciated risk factors. The multi-factorial nature of inhibitor development makes it difficult to ascertain the contribution of FVIII products in inhibitor development through individual PUP studies. Sufficiently powered studies of large cohorts may overcome these limitations but interpretations should be conducted cautiously. Proper design and implementation of PUP safety studies will become even more important with the introduction of new molecules, such as extended half-life or human cell-line derived FVIII that propose reduced immunogenicity. Despite these difficulties, carefully performed clinical studies in PUPs may provide important insights into the natural history of the immune response to FVIII and may suggest targets for intervention to reduce immunogenicity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 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".