Switching to nonacog beta pegol in hemophilia B: Outcomes from a Canadian real‐world, multicenter, retrospective study
Bibliographic record
Abstract
Background: The Canadian Bleeding Disorders Registry (CBDR) captures data from 24 hemophilia treatment centers and patients directly. Nonacog beta pegol (N9-GP) was approved in Canada in 2018. Objectives: To assess treatment outcomes following switching to N9-GP in a real-world setting. Methods: CBDR data for Canadian male patients (aged 7-72 years) with hemophilia B receiving prophylactic N9-GP for ≥6 months as of March 31, 2021, were included. To allow comparison with the previously used products, only patients for whom data were available in the CBDR for at least 6 months before the switch to N9-GP were included in this retrospective analysis. Results: Forty-two patients were included in the analysis (total observation period: 148.0 patient-years). The distribution of disease severity was 62% severe, 36% moderate, 2% mild, with 62% of patients previously receiving recombinant factor IX-Fc-fusion protein (rFIXFc) and 38% previously receiving standard half-life (SHL) recombinant factor IX (rFIX). During a median follow-up period of 2.3 years on N9-GP prophylaxis, 232 bleeds were reported in 30 patients, 29% of patients reported zero bleeds. The median overall annualized bleeding rate on N9-GP was 0.73 for patients switching from rFIXFc (previously 1.44) and 2.10 for patients switching from SHL rFIX (previously 6.06). Median total annualized factor consumption (IU/kg) was lower with N9-GP than with previous SHL rFIX (2152 vs 3018) and previous rFIXFc (1766 vs 2278). Conclusions: Results from this first real-world study of N9-GP in patients with hemophilia B suggest optimal bleeding control with low factor consumption after switching to N9-GP, irrespective of the previous product.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".