Low‐dose immune tolerance induction alone or with immunosuppressants according to prognostic risk factors in Chinese children with hemophilia A inhibitors
Bibliographic record
Abstract
BACKGROUND: In developing countries, children with hemophilia A (HA) with high-titer inhibitor and poor immune tolerance induction (ITI) prognostic risk(s) cannot afford the recommended high- or intermediate-dose ITI. OBJECTIVES: To determine the efficacy of low-dose ITI (plasma-derived factor VIII [FVIII]/von Willebrand factor at 50 FVIII IU/kg every other day) by itself (ITI-alone) or combined with immunosuppressants rituximab and prednisone (ITI-IS) in children with HA with high-titer inhibitor. METHODS: All enrolled patients had pre-ITI inhibitor ≥10 BU. We used ITI-alone if inhibitor titer was <40 BU pre-ITI and during ITI, and ITI-IS if titer was ≥100 BU (historic) or ≥40 BU (pre- or during ITI) or if the patient was nonresponsive on ITI-alone. RESULTS: Fifty-six children were analyzable, with median historic peak inhibitor titer 48.0 BU and followed for median 31.4 months. Overall, 35 (62.5%) achieved phase 2 success with negative inhibitor and normal FVIII recovery. The phase 2 success rate was 95% for the 20 patients receiving ITI-alone. For the 36 patients receiving ITI-IS, the phase 2 success rate was 44.4%, but would increase to 63.6% if the 14 patients with historic peak inhibitor titer ≥100 BU (and having phase 2 success rate of only 14.3%) were excluded. One patient developed repeated infection after IS treatment. Relapse occurred in 11.4% (4/35) patients with phase 2 success associated with rapid ITI dose reduction or irregular post-ITI FVIII prophylaxis. Our strategy reduced the cost from high-dose ITI by 74% to 90%. CONCLUSION: The use of low-dose ITI with or without immunosuppressants according to ITI prognostic risk(s) is a clinically and economically feasible strategy for eradicating inhibitors in children with HA, particularly for those with historic peak inhibitor titer <100 BU.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".