Immunotherapy in Patients with Advanced Non-Small-Cell Lung Cancer Under-Represented by Clinical Trials
Bibliographic record
Abstract
Since the initial US FDA approval of an immune checkpoint inhibitor (ICI) for the treatment of non-oncogene-driven non-small-cell lung cancer (NSCLC) nine years ago, this therapeutic strategy has been cemented as a crucial component of treatment for most of these patients. However, there is a clear efficacy–effectiveness gap whereby patients in the ‘real world’ seem to have more modest clinical outcomes compared to those enrolled in landmark clinical trials. This gap may be driven by the under-representation of important patient populations, including populations defined by clinical or molecular characteristics. In this review, we summarize the data outlining the evidence of ICIs in patients with poor Eastern Cooperative Oncology Group performance status (ECOG PS), underlying autoimmune disease (AID), older age, active brain metastases (BMs), and molecular aberrations such as EGFR mutations, ALK fusions, BRAF mutations and ROS1 fusions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".