Incidence of serious infection among etanercept and infliximab initiators: safety comparison between biosimilars and bio-originators with Canadian population-based data
Bibliographic record
Abstract
BACKGROUND: Safety remains a significant concern for biologic drugs, and studies are needed to ensure a comparable safety profile for biosimilars and their legacy treatments. Using Canadian administrative health data from 2015-2019, we compared the incidence of serious infection between biosimilars and bio-originators initiators for etanercept and infliximab, two of the most commonly used biologics during this time. METHODS: We performed a retrospective cohort study using pan-Canadian data (except Quebec) from the National Prescription Drug Utilization Information System linked to hospitalization data. We studied new users of infliximab or etanercept (January/2015-December/2019) and compared incidence rates of serious infection, defined as those which required hospitalization, by using Cox regression models adjusted by biological sex, age at treatment initiation, prior corticosteroid or biologic, province, and calendar year. RESULTS: We studied 6,583 etanercept users (mean age 62) and 7,202 infliximab users (mean age 45). Hospitalization with infections occurred in 7% of infliximab and 2% of etanercept users. Comparing the risk of infection between biosimilar to bio-originator, the adjusted hazard ratio (95% confidence interval) was 1.33 (0.77, 2.30) for etanercept and 0.93 (0.72, 1.18) for infliximab. CONCLUSIONS: Our study found no clear difference between etanercept and infliximab biosimilars and their bio-originators for infection incidence, suggesting a similar safety profile.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".