Real-World Safety and Effectiveness of a Bevacizumab Biosimilar (ABP 215) in Metastatic Colorectal Cancer Patients in Canada
Bibliographic record
Abstract
BACKGROUND: ABP 215 is a biosimilar to the reference product, bevacizumab, and was one of the first biosimilars approved by Health Canada for the first-line treatment of metastatic colorectal cancer (mCRC). This study aimed to address gaps in real-world evidence (RWE) including patient characteristics, treatment safety (primary objective), and effectiveness (secondary objective) for first-line ABP 215 therapy in Canadian patients with mCRC. MATERIALS AND METHODS: Retrospective data were collected in 2 waves, at least 1 year (Wave 1) or 2 years (Wave 2) after commercial availability of ABP 215 at each participating site. RESULTS: A total of 75 patients from Wave 1 and 164 patients from Wave 2 treated with a minimum of 1 cycle of ABP 215 were included. At least one safety event of interest (EOI) was recorded for 34.7% of Wave 1 and 42.7% of Wave 2 patients. The median progression free survival (PFS) for Wave 1 and 2 patients were 9.47 (95% confidence interval [CI]: 6.71, 11.90) and 21.38 (95% CI: 15.82, not estimable) months, respectively. Median overall survival was not estimable for Wave 1 and was 26.45 months for Wave 2. CONCLUSION: The safety and effectiveness of ABP 215 observed in this real-world study were comparable to clinical trial findings and to other RWE with longer PFS in the current study.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".