Increased inhibitor incidence in severe haemophilia A since 1990 attributable to more low titre inhibitors
Bibliographic record
Abstract
Many studies have reported an increased incidence of inhibitors in previously untreated patients (PUPs) with severe haemophilia A after the introduction of recombinant products. It was the objective of this study to investigate whether the inhibitor incidence has increased between 1990 and 2009 in an unselected cohort of PUPs with severe haemophilia A (FVIII< 1 %). Patients were consecutively recruited from 31 haemophilia treatment centres in 16 countries and followed until 50 exposure days or until inhibitor development. Inhibitor development was studied in five-year birth cohorts comparing cumulative incidences. Furthermore the risk for inhibitor development per five-year birth cohort was studied using multivariable Cox regression, adjusting for potential genetic and treatment-related confounders. A total of 926 PUPs were included with a total cumulative inhibitor incidence of 27.5 %. The inhibitor incidence increased from 19.5 % in 1990-1994 (lowest) to 30.9 % in 2000-2004 (highest; p-value 0.011). Low titre inhibitor incidence increased from 3.1 % in 1990-1994 to 10.5 % in 2005-2009 (p-value 0.009). High titre inhibitor incidences remained stable over time. After 2000, risk of all inhibitor development was increased with adjusted hazard ratios 1.96 (95 % CI 1.06-2.83) in 2000-2004 and 2.34 (1.42-4.92) in 2005-2009. Screening for inhibitors was intensified over this 20-year study period from a median of 1.9 to 2.9 tests/year before 2000 to 2.7 to 4.3 tests/year after 2000. In conclusion, the cumulative inhibitor incidence has significantly increased between 1990 and 2009. The high titre inhibitor incidence has remained stable.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".