Key questions in the new hemophilia era: update on concomitant use of FVIII and emicizumab in hemophilia A patients with inhibitors
Bibliographic record
Abstract
Introduction: Immune tolerance induction (ITI) is the primary therapeutic strategy and only proven method to eradicate inhibitors to coagulation factor VIII (FVIII) in hemophilia A. Emicizumab, a humanized bispecific monoclonal antibody that mimics the function of activated FVIII, has expanded options to treat hemophilia A. The availability of emicizumab necessitates a revisit of recommendations for managing patients with inhibitors.Areas covered: Current evidence is reviewed about the concomitant use of emicizumab and FVIII concentrates during and after ITI. Areas where data are lacking are highlighted and ongoing studies designed to address these issues are described.Expert opinion: Inhibitor eradication remains a desirable goal. All patients with inhibitors should be offered at least one attempt at ITI. Emicizumab monotherapy is an option for inhibitor patients who are not candidates for ITI. Evidence is emerging about the use of emicizumab during ITI to prevent bleeds. Studies are currently addressing the safety, efficacy, and feasibility of concomitant emicizumab and FVIII in ITI. As evidence regarding the risk of inhibitor recurrence and need for continued FVIII to maintain immune tolerance post-ITI is limited, the role of emicizumab alone or in combination with FVIII after ITI is the subject of an upcoming study.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".