Overview of Systematic Reviews of ICIs in NSCLC With EGFR, ALK, ROS1, and RET Actionable Driver Mutations
Bibliographic record
Abstract
The effectiveness and safety of immune checkpoint inhibitor (ICI) monotherapy in patients with previously treated advanced or metastatic non–small cell lung cancer with EGFR, ALK, ROS1, or RET actionable driver mutations or chromosomal rearrangements is currently uncertain. We assessed the efficacy and safety of ICIs in patients with this condition whose disease did not respond well to previous chemotherapy. We reviewed 13 systematic reviews of randomized controlled trials (RCTs). The quality assessment of these reviews revealed critical methodological flaws. All 13 systematic reviews focused on survival and progression-free survival (PFS) for patients with non–small cell lung cancer and EGFR gene mutations. The systematic reviews generally considered the same set of 4 clinical trials and did not report on other outcomes or patient groups, except for 1 review that looked at patients with different levels of anti-PD-L1 expression. We found no evidence on the efficacy and safety of ICIs in patients with ALK, ROS1, or RET mutations. Overall, the systematic reviews concluded that using ICIs alone, as a second-line therapy or beyond, does not significantly improve overall survival (OS) and PFS compared to chemotherapy in patients with non–small cell lung cancer with EGFR gene mutations. No conclusions can be made regarding the benefits of ICIs in patients with EGFR mutations based on histology or high antiprogrammed death-ligand 1 antibody expression levels. The safety of ICIs in patients with EGFR, ALK, ROS1, or RET actionable driver mutations could not be assessed because of the lack of evidence provided in the included systematic reviews.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".