OC 59.1 Concizumab Prophylaxis in Patients with Haemophilia A or B without Inhibitors: Efficacy and Safety Results from the Primary Analysis of the Phase 3 Explorer8 Study
Bibliographic record
Abstract
extravascular space.In murine HB models the presence (CRM+) or absence (CRM-) of dysfunctional FIX can impact the PK (and in turn hemostatic efficacy) of the therapeutic FIX.We hypothesized that the PK parameters of EHL FIX products that have a significant (rFIX-Fc) or negligible (N9-GP) extravascular distribution might be differently influenced by the presence of FIX antigen in the extravascular space.Aims: To investigate the effect of F9 mutations on the PK profile of patients treated with EHL FIX concentrates.Methods: Bayesian PK estimates on patients >12 yo from participating Canadian centers were extracted from the WAPPS-Hemo database.Genetic mutations for the same patients were obtained for the same patients from the Canadian Bleeding Disorders Registry.Patients with missense mutations were classified as CRM+, all the others were considered CRM-.Differences in PK parameter distributions between CRM+ and CRM-groups were analyzed by non-parametric Wilcoxon test.Results: A total of 69 individual PK studies were available for analysis.Clearance, Terminal half-life, In vivo recovery, and Volume of distribution were all significantly reduced in CRM-patients that received rFIX-Fc (Fig. 1).No statistically significant differences were found between patients receiving N9-GP (Fig. 1).Conclusion(s): Patients with F9 missense mutations may express levels of FIX antigen that could compete with the exogenous FIX and influence PK parameters.This phenomenon may not take place when a FIX product with an exclusive intravascular distribution is administered.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".