Canadian Consensus Recommendations on the Management of MET-Altered NSCLC
Bibliographic record
Abstract
In Canada, the therapeutic management of patients with advanced non-small cell lung cancer (NSCLC) with rare actionable mutations differs between provinces, territories, and individual centres based on access to molecular testing and funded treatments. These variations, together with the emergence of several novel mesenchymal-epithelial transition (MET) factor-targeted therapies for the treatment of NSCLC, warrant the development of evidence-based consensus recommendations for the use of these agents. A Canadian expert panel was convened to define key clinical questions, review evidence, discuss practice recommendations and reach consensus on the treatment of advanced MET-altered NSCLC. Questions addressed by the panel include: 1. How should the patients most likely to benefit from MET-targeted therapies be identified? 2. What are the preferred first-line and subsequent therapies for patients with MET exon 14 skipping mutations? 3. What are the preferred first-line and subsequent therapies for advanced NSCLC patients with de novo MET amplification? 4. What is the preferred therapy for patients with advanced epidermal growth factor receptor (EGFR)-mutated NSCLC with acquired MET amplification progressing on EGFR inhibitors? 5. What are the potential strategies for overcoming resistance to MET inhibitors? Answers to these questions, along with the consensus recommendations herein, will help streamline the management of MET-altered NSCLC in routine practice, assist clinicians in therapeutic decision-making, and help ensure optimal outcomes for NSCLC patients with MET alterations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.009 | 0.003 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".