Real-World Study to Assess Patterns of Treatment Practices and Clinical Outcomes in Metastatic Colorectal Cancer Patients with RAS Wild-Type Left-Sided Tumours in Canada
Bibliographic record
Abstract
Minimal Canadian data are available on the RAS testing rates, treatment patterns, and corresponding overall survival (OS) in metastatic colorectal cancer (mCRC) patients. We conducted a population-based cohort study of left-sided RAS wild-type (WT) mCRC patients diagnosed between 1 January 2014 and 31 December 2019, and who were treated with first-line (1L) chemotherapy plus the epidermal growth factor receptor inhibitor panitumumab, chemotherapy plus bevacizumab, or chemotherapy alone, in Alberta, Canada, using electronic medical records and administrative health system data. Of the 2721 patients identified with left-sided mCRC, 320 patients with RAS WT mCRC were treated with 1L systemic therapy: chemotherapy plus panitumumab (n = 64), chemotherapy plus bevacizumab (n = 52), or chemotherapy alone (n = 204). Only 65% and 39% of the 320 1L-treated patients initiated second- and third-line therapy, respectively. A total of 71% of individuals with treated left-sided mCRC underwent RAS testing. The median OS for mCRC patients with RAS WT left-sided tumours was higher for patients treated with 1L panitumumab plus chemotherapy (34.3 months; 95% CI: 23.8-39.6) than for patients who received 1L chemotherapy alone (30.0 months; 95% CI: 24.9-34.1) or 1L bevacizumab plus chemotherapy (25.6 months; 95% CI: 21.2-35.7). These findings highlight an unmet need in left-sided RAS WT mCRC, with relatively few individuals receiving a biologic agent in combination with chemotherapy in the 1L setting, a high rate of attrition between lines, and a need for increased RAS testing before treatment initiation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".