P672 Occurrence of malignancy among infliximab biosimilar and bio-originator initiators in Canada: a comparative population-based analysis
Bibliographic record
Abstract
Abstract Background Infliximab (INF) downregulates the immune system. Given the immune system's role in deleting pre-cancerous cells, malignancy is a potential concern. Still, relatively few real-world data analyses have compared biosimilar versus bio-originator INF in terms of malignancy incidence. We aimed to compare malignancy occurrence among initiators of infliximab biosimilar (INF-B) and originator (INF-O). Methods We used data from the National Prescription Drug Utilization Information System (NPDUIS), containing pan-Canadian (except Quebec) claims-level data on prescription dispensations paid from public drug programs linked to comprehensive day surgery and inpatient care (Discharge Abstract Database, DAD) and hospital-based and community-based ambulatory care (National Ambulatory Care Reporting System, NACRS). We studied adults (≥18 years) initiating INF between January 2015 and December 2019 with no history of malignancy, HIV or organ transplant (1-year pre-baseline). Follow-up began 365 days after treatment initiation (lag period) and continued until the first cancer diagnostic code (ICD-10 C00-C97), treatment discontinuation + 365 days, or end of study period. We compared INF-B and INF-O using Cox regression models, adjusting for potential confounders/ effect modifiers: sex, age at treatment initiation, prior use of other biologics or prednisone, province (Ontario versus others), and calendar year. Results New users of INF-B (2,019) and INF-O (5,183) were about half female (52%), with a median age (interquartile range) of 45 years (28-62). Overall, the malignancy incidence rate was 10.2 (95% confidence interval, CI, 8.7-11.8) events per 1,000 person-years. Comparing INF-B to INF-O, the adjusted hazard ratio for malignancy was 0.97 (95% CI 0.63-1.48). Conclusion In this real-world dataset, we were unable to identify clear differences in malignancy comparing INF-B and INF-O. Limitations include the inability to control for residual confounders (e.g., disease severity), potential outcome misclassification, and selection bias.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".