The Role of Immunomodulation in the Management of Factor VIII Inhibitors
Bibliographic record
Abstract
Approximately 25% of persons with hemophilia A will have their treatment complicated by the development of anti-FVIII inhibitory antibodies. This adverse event requires the use of alternative hemostatic agents to treat bleeding and the consideration of a protocol to generate immunological tolerance to FVIII. The pathogenetic factors contributing to FVIII inhibitor generation include both patient- and concentrate-related characteristics. The FVIII genotype contributes to this risk as do other, less well defined, immunogenetic factors. The role of the type of FVIII concentrate as a precipitant for inhibitor generation appears to be less influential. Immunomodulatory management of FVIII inhibitors requires sustained and repeated exposure to FVIII through a variety of intravenous immune tolerance induction (ITI) protocols. Certain pre-ITI characteristics predict for the likelihood of success, most especially the pre-ITI anti-FVIII inhibitor titer. Currently, two major areas of debate remain unresolved with relation to the optimal form of ITI schedule. The best FVIII dose to generate FVIII tolerance is under investigation in an international prospective trial, while the issue of whether von Willebrand factor-containing concentrates may provide more powerful tolerizing effects remains open for further discussion. With a variety of ITI protocols, success rates of approximately 80% have been achieved with good-risk patients. In those that fail initial attempts at ITI, additional treatments using agents such rituximab are now being explored with further evidence of success in 60-80% of these salvage patients. Finally, several pre-clinical studies of innovative approaches to achieving FVIII tolerance suggest that combinations of immunomodulatory therapy may be of benefit in the future.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".