Integrating biosimilars into oncology practice
Bibliographic record
Abstract
In recent years, biologic therapies have increasingly been used in oncology and for other medical conditions, placing a significant economic burden on the healthcare systems (Cohen, 2017; Godman et al., 2018). In light of rising healthcare costs, biosimilars, more affordable biologics, have been introduced on the market to meet existing needs and facilitate biologic treatment access for all patients. In Canada, the combined savings from the use of etanercept, filgrastim, infliximab and insulin glargine biosimilars in 2018 totaled $94 million (Patented Medicine Prices Review Board, 2018). While the use of biosimilar medicines has been successfully implemented in Europe for 15 years (Health Canada, 2019), the first oncology biosimilar, a biosimilar of bevacizumab, was approved by Health Canada in 2018 (Generics and Biosimilar Initiative, 2021). Questions remain amongst healthcare providers about the efficacy and safety of biosimilars in oncology (Rugo et al., 2018). Furthermore, variability in the regulatory framework for biosimilars across Canadian provinces might complicate their introduction to clinical practice. With the current article, we aim to provide oncology nurses with an overview of biosimilars in Canada and address common myths and misconceptions about this class of medications.
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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 | 0.049 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".