Therapy for Stage IV Non–Small-Cell Lung Cancer With Driver Alterations: ASCO Living Guideline
Bibliographic record
Abstract
Living guidelines are routinely updated guidelines that are developed for selected topic areas with rapidly evolving evidence that drives frequent change in clinical practice. These guidelines are updated on a regular schedule, based on the work of a standing panel that reviews the literature on a continuous basis. Updates will be made regularly and can be found at https://ascopubs.org/nsclc-da-living-guideline . PURPOSE To provide evidence-based recommendations updating the 2021 ASCO and Ontario Health (Cancer Care Ontario) guideline on systemic therapy for patients with stage IV non–small-cell lung cancer (NSCLC) with driver alterations. METHODS ASCO updated recommendations on the basis of an ongoing systematic review of randomized control trials from 2020 to 2021. RESULTS This guideline update reflects changes in evidence since the previous update. Two studies provide the evidence base. Outcomes of interest include efficacy and safety. RECOMMENDATIONS For patients with an anaplastic lymphoma kinase rearrangement, a performance status (PS) of 0-2, and previously untreated NSCLC, clinicians should offer alectinib or brigatinib or lorlatinib. For patients with an anaplastic lymphoma kinase rearrangement, a PS of 0-2, and previously untreated NSCLC, if alectinib, brigatinib, or lorlatinib are not available, clinicians should offer ceritinib or crizotinib. For patients with a RET rearrangement, a PS of 0-2, and previously untreated NSCLC, clinicians may offer selpercatinib or pralsetinib. In second line, for patients with a RET rearrangement who have not received RET-targeted therapy, clinicians may offer selpercatinib or pralsetinib. Additional information is available at www.asco.org/thoracic-cancer-guidelines .
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".