Natural history and clinical characteristics of inhibitors in previously treated haemophilia A patients: a case series
Bibliographic record
Abstract
BACKGROUND: Development of inhibitors is the most serious complication in haemophilia A treatment. The assessment of risk for inhibitor formation in new or modified factor concentrates is traditionally performed in previously treated patients (PTPs). However, evidence on risk factors for and natural history of inhibitors has been generated mostly in previously untreated patients (PUPs). The purpose of this study was to examine cases of de novo inhibitors in PTPs reported in the scientific literature and to the EUropean HAemophilia Safety Surveillance (EUHASS) programme, and explore determinants and course of inhibitor development. METHODS: We used a case series study design and developed a case report form to collect patient level data; including detection, inhibitor course, treatment, factor VIII products used and events that may trigger inhibitor development (surgery, vaccination, immune disorders, malignancy, product switch). RESULTS: We identified 19 publications that reported 38 inhibitor cases and 45 cases from 31 EUHASS centres. Individual patient data were collected for 55/83 (66%) inhibitor cases out of 12 330 patients. The median (range) peak inhibitor titre was 4.4 (0.5-135.0), the proportion of transient inhibitors was 33% and only two cases of 12 undergoing immune tolerance induction failed this treatment. In the two months before inhibitor development, surgery was reported in nine (22%) cases, and high intensity treatment periods reported in seven (17%) cases. CONCLUSIONS: By studying the largest cohort of inhibitor development in PTPs assembled to date, we showed that inhibitor development in PTPs, is on average, a milder event than in PUPs.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".