Effect of targeted therapy and immunotherapy on advanced nonsmall‐cell lung cancer outcomes in the real world
Bibliographic record
Abstract
The evolution of diagnosis and treatment of advanced nonsmall-cell lung cancer (NSCLC) has led to increasing the use of targeted therapy and immune checkpoint inhibitors. The study goal was to assess the effect of molecular testing and the introduction of new therapies on overall survival (OS). All patients with stage IV NSCLC referred to BC Cancer were included in the study. Four 1-year time cohorts were created based on molecular testing implementation and funded drug availability: C1 baseline (2009), C2 EGFR TKI access (2011), C3 ALK inhibitor access (2015), C4 immunotherapy availability (2017). Baseline demographics, disease characteristics, and systemic therapy details were collected retrospectively. OS was calculated using the Kaplan-Meier method and compared using the log-rank test. There were 3421 patients identified with stage IV NSCLC and 1319 (39%) received systemic therapy. In the four 1-year time cohorts C1/C2/C3/C4: driver mutation-targeted treatment increased 1/17/27/34% (of total systemic therapy), as did treatment with any line immunotherapy <1/1/9/38%. Median OS with best supportive care (BSC) was 3.4/3.1/3.2/2.9 m (p = 0.16) and with systemic treatment 9.9/10.9/13.9/15.0 m (p < 0.001). Median OS by treatment exposure was BSC 3.1 m, chemotherapy only 7.3 m, targeted therapy 17.5 m, and immunotherapy 20.7 m. In our real-world study, following the introduction of targeted therapy and immune checkpoint inhibitors, there was a significant improvement in OS in each successive time cohort concordant with advancements in therapeutic options.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".