Impact of the addition of bevacizumab (Bev) to chemotherapy on survival in metastatic colorectal cancer (MCRC): A real-world population-based analysis.
Bibliographic record
Abstract
e18215 Background: The addition of bev to IFL chemotherapy in MCRC was shown to improve survival in clinical trials, but its benefit in the real-world where it is frequently used with other systemic therapy regimens has not been fully evaluated. We aimed to assess the impact of bev on MCRC outcomes in a population-based setting. Methods: We examined all patients diagnosed with MCRC from a large Canadian province between 2004 and 2016. We categorized cases based on receipt of bev. Predictors of bev use were examined with multivariate logistic regression models. We also determined correlates of overall survival (OS) and disease specific survival (DSS) using Kaplan-Meier methods and Cox regression models, adjusting for measured confounders. Results: In total, 1,324 MCRC patients received systemic therapy. Median age was 68 years and 728 (55.0%) were men. Among them, 310 (23.4%) received bev. Younger age and better Charlson comorbidity score were associated with a higher likelihood of bev treatment (all p < 0.05). In multivariate analysis, bevacizumab receipt did not correlate with OS or DSS for the entire cohort or for those who received bev with single agent chemotherapy (see Table). However, use of bev was correlated with improved outcomes for patients treated with oxaliplatin-based chemotherapy (HR for OS: 0.751 [0.571 – 0.987], p = 0.040; HR for DSS: 0.680 [0.502 – 0.920], p = 0.013), but worse outcomes for irinotecan-based chemotherapy (HR for OS: 1.327 [1.083 – 1.628], p = 0.006; HR for DSS: 1.309 [1.054 – 1.627], p = 0.015). Conclusions: Bev was only associated with improved survival when used with specific chemotherapy regimens. Table: Univariate and multivariate survival analysis for MCRC patients based on receipt of bevacizumab. m = median; (m) = months; HR = hazard ratio [Table: see text]
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".