Determinants of successful immune tolerance induction in hemophilia A: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Immune tolerance induction (ITI) aims to eradicate anti-factor VIII (FVIII) antibodies (inhibitors) in persons with hemophilia A. However, this burdensome treatment fails in 10% to 40%. To estimate the chance of ITI success in clinical decision making, it is important to identify the predictors of ITI success. Objectives: We performed a systematic review and meta-analysis to summarize the current evidence on determinants of ITI outcome in persons with hemophilia A. Methods: A literature search was conducted to identify randomized controlled trials, cohort, or case-control studies reporting on the predictors for ITI outcome in persons with hemophilia A. The main outcome was ITI success. Methodological quality was assessed using an adapted Joanna Briggs Institute checklist, rating as high if ≥11 of 13 criteria were met. Pooled odds ratios (ORs) for ITI success were calculated for each determinant. ITI success was defined as negative inhibitor titer (<0.6 BU/mL), FVIII recovery ≥66% of expected, and FVIII half-life ≥6 hours in 16 (59.3%) studies. Results: We included 27 studies, involving 1,734 participants. Methodological quality of 6 (22.2%) studies (418 participants) was rated as high. Twenty different determinants were assessed. Historical peak titer ≤100 BU/mL (compared with >100 BU/mL, OR, 1.7; 95% CI, 1.4-2.1), pre-ITI titer ≤10 BU/mL (compared with >10 BU/mL, OR, 1.8; 95% CI, 1.4-2.3), and peak titer during ITI ≤100 BU/mL (compared with >100 BU/mL, OR, 2.7; 95% CI, 1.9-3.8) were associated with a higher chance of ITI success. Conclusion: Our results suggest that determinants related to the inhibitor titer are associated with ITI success.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".