The changing face of immune tolerance induction in haemophilia A with the advent of emicizumab
Bibliographic record
Abstract
INTRODUCTION: As a result of the new treatment paradigm that the haemophilia community will face with the availability of novel (non-factor) therapies, an updated consensus on ITI recommendations and inhibitor management strategies is needed. AIM: The Future of Immunotolerance Treatment (FIT) group was established to contemplate, determine and recommend the best management options for patients with haemophilia A and inhibitors. DISCUSSION AND CONCLUSIONS: Despite the considerable success of emicizumab in the management of inhibitor patients, the FIT group still sees the importance of eradicating inhibitors. However, the availability of emicizumab and other non-factor therapies in the future might impact greatly on how ITI is undertaken. Theoretically, concomitant use of emicizumab and FVIII might allow emicizumab to effectively prevent bleeding with lower dose ITI regimens. This might allow for the greater adoption of low-dose/low-frequency FVIII ITI regimens, which may result in a reduced need for central venous access devices while still maintaining a reasonable likelihood of ITI success. The FIT group proposes a new management algorithm for current ITI (without emicizumab) and a hypothetical new approach with the availability of emicizumab. As there are no published data regarding the concomitant use of emicizumab and FVIII for ITI, the FIT Expert group encourages the undertaking of properly conducted prospective studies to explore these approaches further.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".