Canadian Consensus: A New Systemic Treatment Algorithm for Advanced EGFR-Mutated Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Background: Multiple clinical trials for the treatment of advanced EGFR-mutated non-small-cell lung cancer (nsclc) have recently been reported. As a result, the treatment algorithm has changed, and many important clinical questions have been raised: (1) What is the optimal first-line treatment for patients with EGFR-mutated nsclc? (2) What is preferred first-line treatment for patients with brain metastasis? (3) What is the preferred second-line treatment for patients who received first-line first- or second-generation tyrosine kinase inhibitors (tkis)? (4) What is the preferred treatment after osimertinib? (5) What evidence do we have for treating patients whose tumours harbour uncommon EGFR mutations? Methods: A Canadian expert panel was convened to define the key clinical questions, review recent evidence, and discuss and agree on practice recommendations for the treatment of advanced EGFR-mutated nsclc. Results: The published overall survival results for osimertinib, combined with its central nervous system activity, have led to osimertinib becoming the preferred first-line treatment for patients with common EGFR mutations, including those with brain metastasis. Other agents could still have a role, especially when osimertinib is not available or not tolerated. Treatment in subsequent lines of therapy depends on the first-line therapy or on T790M mutation status. Treatment recommendations for patients whose tumours harbour uncommon EGFR mutations are guided mainly by retrospective and limited prospective evidence. Finally, the evidence for sequencing and combining tkis with chemotherapy, angiogenesis inhibitors, checkpoint inhibitors, and other new therapeutics is reviewed. Conclusions: This Canadian expert consensus statement and algorithm were driven by significant advances in the treatment of EGFR-mutated nsclc.
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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 | 0.044 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.011 | 0.006 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".