Current Landscape of Immunotherapy for the Treatment of Metastatic Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
For patients with advanced non-small-cell lung cancer (nsclc) lacking a targetable molecular driver, the mainstay of treatment has been cytotoxic chemotherapy. The survival benefit of chemotherapy in this setting is modest and comes with the potential for significant toxicity. The introduction of immunotherapeutic agents targeting the programmed cell death 1 protein (PD-1) and the programmed cell death ligand 1 (PD-L1) has drastically changed the treatment paradigms for these patients. Three agents-atezolizumab, nivolumab, and pembrolizumab-have been shown to be superior to chemotherapy in the second-line setting. For patients with tumours strongly expressing PD-L1, pembrolizumab has been associated with improved outcomes in the first-line setting. Demonstration of the significant benefits of immunotherapy in nsclc has focused attention on new questions. Combination checkpoint regimens, with acceptable toxicity and potentially enhanced efficacy, have been developed, as have combinations of immunotherapy with chemotherapy. In this review, we focus on the published trials that have changed the treatment landscape in advanced nsclc and on the ongoing clinical trials that offer hope to further improve outcomes for patients with advanced 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".