Treatment related factors and inhibitor development in children with severe haemophilia A
Bibliographic record
Abstract
With the advent of modern factor replacement therapy the most important remaining obstacle to successful treatment in haemophilia A is the development of inhibitory antibodies against Facto VIII (FVIII). This retrospective case control study examined genetic variables and early treatment patterns in severe haemophilia A patients who subsequently developed clinically significant inhibitors to FVIII compared with matched controls who did not. Seventy eight inhibitor patients were identified from 13 UK centers over 25 years (1982-2007). For each case an age matched control was selected. Data on potential genetic and treatment related risk factors were collected for cases and controls. Treatment related data was collected for the first 50 exposure days (EDs) for controls or up to inhibitor development for cases. Risk factors were compared for significance by univariate and multivariate analysis. Of the genetic risk factors, major defects in the FVIII gene and non-caucasian ethnicity were each responsible for approximately 5-fold increases in inhibitor risk. When treatment related variables are considered, high intensity treatment increased inhibitor risk around 2.5 fold whether represented by the presence of peak treatment moments or by high overall treatment frequency. This finding was significant regardless of the timing of the high intensity treatment. Periods of intense treatment associated with surgery for porta-cath insertion were however not found to be associated with increased inhibitor risk. No association was shown between inhibitor development and age at first FVIII exposure, type of FVIII product, or the use of regular prophylaxis. This study confirms treatment-related factors as important risks for inhibitor development in Haemophilia A.
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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 | 0.000 | 0.003 |
| 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".