Immune complications and their management in inherited and acquired bleeding disorders
Bibliographic record
Abstract
Disorders of coagulation, resulting in serious risks for bleeding, may be caused by autoantibody formation or by mutations in genes encoding coagulation factors. In the latter case, antidrug antibodies (ADAs) may form against the clotting factor protein drugs used in replacement therapy, as is well documented in the treatment of the X-linked disease hemophilia. Such neutralizing antibodies against factors VIII or IX substantially complicate treatment. Autoantibody formation against factor VIII leads to acquired hemophilia. Although rare, antibody formation may occur in the treatment of other clotting factor deficiencies (eg, against von Willebrand factor [VWF]). The main strategies that have emerged to address these immune responses include (1) clinical immune tolerance induction (ITI) protocols; (2) immune suppression therapies (ISTs); and (3) the development of drugs that can improve hemostasis while bypassing the antibodies against coagulation factors altogether (some of these nonfactor therapies/NFTs are antibody-based, but they are distinct from traditional immunotherapy as they do not target the immune system). Choice of immune or alternative therapy and criteria for selection of a specific regimen for inherited and autoimmune bleeding disorders are explained. ITI serves as an important proof of principle that antigen-specific immune tolerance can be achieved in humans through repeated antigen administration, even in the absence of immune suppression. Finally, novel immunotherapy approaches that are still in the preclinical phase, such as cellular (for instance, regulatory T cell [Treg]) immunotherapies, gene therapy, and oral antigen administration, are discussed.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".