Current and Future Approaches to Inhibitor Management and Aversion
Bibliographic record
Abstract
Immune tolerance induction (ITI) is the most common approach used to eliminate inhibitors that develop in hemophilia A patients following exposure to factor (F) VIII therapy. ITI generally requires ongoing long-term exposure to factor replacement therapy using FVIII or FIX. Although plasma-derived products have been the mainstay of ITI therapy in the past, recent data indicate that high-purity (i.e., recombinant) rFVIII products are probably equally effective. For patients who have failed to respond to ITI treatment, or for those at high risk to do so, immunosuppressive therapy may be helpful. Rituximab has demonstrated a possible clinical benefit in hemophilic and nonhemophilic patients developing FVIII inhibitors, but benefit in those with congenital hemophilia and inhibitors has not been established and more extensive clinical studies are needed. More recently, research on reducing the incidence of inhibitor development has included mutagenizing key epitopes of the FVIII antigenic molecule to alter its immunogenicity without affecting biological activity, as well as induction of tolerance by gene therapy with immunodominant A2 and C2 domains of FVIII presented by B cells as immunoglobulin fusion proteins.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".