Real-world use of biologics and its association with outcomes in RAS wild-type (WT) left-sided metastatic colorectal cancer (mCRC) in Canada.
Bibliographic record
Abstract
56 Background: The use of biologics in combination with chemotherapy early in the course of treatment in mCRC improves outcomes, but whether this approach is uniformly adopted in clinical practice is unclear. This study aims to describe biomarker testing, treatment patterns, and survival of RAS WT, left-sided mCRC patients in a real-world population. Methods: We performed deterministic linkages with population-based data sources, including the cancer registry, pharmacy data, electronic medical records, and vital statistics to identify all patients who were diagnosed with RAS WT, left-sided mCRC, and treated with first-line (1L) combination systemic therapy from 2014 to 2019 in Alberta, Canada. Patients were followed until December 31, 2020. Results: Of 2,721 patients with left-sided mCRC, 1,375 were referred and received systemic therapy, 977 underwent RAS testing prior to or within 30 days of initiating 1L treatment, and 420 were found to be RAS WT. Among them, 320 were treated with 1L combination systemic therapy: FOLFOX/CAPOX/FOLFIRI (n=204), panitumumab (pani) with doublet chemotherapy (n=64), or bevacizumab (bev) with doublet chemotherapy (n=52). The interval from diagnosis to RAS test results was 38 (IQR 24-61) days. Only 207 (65%) and 125 (39%) of the 320 1L-treated patients reached 2L and 3L therapy, respectively. Median overall survival based on the different 1L regimens are summarized. Conclusions: Many real-world patients with mCRC were not exposed to biologics as part of their front-line treatment. There is a need to enhance RAS testing in 1L as 29% of patients were not tested, despite recommendations from guidelines. Because attrition across lines of therapy is high in mCRC, the opportunity to receive and benefit from biologics in later lines of therapy may be importantly reduced. [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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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".