Intensity of factor VIII treatment and inhibitor development in children with severe hemophilia A: the RODIN study
Bibliographic record
Abstract
The objective of this study was to examine the association of the intensity of treatment, ranging from high-dose intensive factor VIII (FVIII) treatment to prophylactic treatment, with the inhibitor incidence among previously untreated patients with severe hemophilia A. This cohort study aimed to include consecutive patients with a FVIII activity < 0.01 IU/mL, born between 2000 and 2010, and observed during their first 75 FVIII exposure days. Intensive FVIII treatment of hemorrhages or surgery at the start of treatment was associated with an increased inhibitor risk (adjusted hazard ratio [aHR], 2.0; 95% confidence interval [CI], 1.3-3.0). High-dose FVIII treatment was associated with a higher inhibitor risk than low-dose FVIII treatment (aHR, 2.3; 95% CI, 1.0-4.8). Prophylaxis was only associated with a decreased overall inhibitor incidence after 20 exposure days of FVIII. The association with prophylaxis was more pronounced in patients with low-risk F8 genotypes than in patients with high-risk F8 genotypes (aHR, 0.61, 95% CI, 0.19-2.0 and aHR, 0.85, 95% CI, 0.51-1.4, respectively). In conclusion, our findings suggest that in previously untreated patients with severe hemophilia A, high-dosed intensive FVIII treatment increases inhibitor risk and prophylactic FVIII treatment decreases inhibitor risk, especially in patients with low-risk F8 mutations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".