Type and intensity of FVIII exposure on inhibitor development in PUPs with haemophilia A
Bibliographic record
Abstract
The impact of treatment-related factors on inhibitor development in previously untreated patients (PUPs) with haemophilia A is still debated. We present the results of a collaborative, individual patient data meta-analytic project. Eligible data sources were published cohorts of PUPs for which patient-level data were available. The exposures of interest were factor (F)VIII type (recombinant [rFVIII] vs plasma-derived [pdFVIII]) and treatment intensity (≥ vs < 150 IU/kg/week) at first treatment. Family history of inhibitors, F8 mutations, age, treatment regimen (on-demand vs prophylaxis), secular trend and surgery were analysed as putative confounders using different statistical approaches (multivariable Cox regression, propensity score analyses, CART). Analyses accounted for the multi-centre origin of the data. We included 761 consecutive, unselected PUPs with moderate to severe haemophilia A from 10 centres in Egypt, Germany, Israel and Italy. A total of 27 % of patients developed inhibitors; 40 % and 22 % of patients treated with rFVIII and pdFVIII (unadjusted HR 2.2, 95 % CI 1.6-2.9), respectively; 51 % and 24 % of patients receiving high- and low-intensity treatment (unadjusted HR 2.9, 95 % CI 2.0-4.2), respectively. In adjusted analyses, only treatment intensity remained an independent predictor; the effect of FVIII type was largely due to confounding, but with a significant interaction between FVIII type and treatment intensity. This patient-level meta-analysis confirms, across different statistical approaches, that high-intensity treatment is a strong risk factor for inhibitor development. The possible role of FVIII type in subgroups is suggested by the test for interactions but could not be proven because of the limited subgroups sample sizes.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.019 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".