Evolution of Systemic Treatment Uptake and Survival in Advanced Non-Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) commonly presents at advanced stage. We previously reported systemic treatment uptake in stage IV NSCLC climbing from 55% (2009-2012) to 62% (2015-2017). Since then, first-line immunotherapy and 2nd/3rd generation tyrosine kinase inhibitors (TKIs) have emerged as standards of care. We explored whether treatment rates continued to rise and studied outcomes. METHODS: We reviewed all cases of de novo stage IIIB/IIIC/IV NSCLC seen in out-patient medical oncology consultation at our institution between 2009-2012 (cohort A), 2015-2017 (cohort B), and June-December 2018 (cohort C). We compared rates of systemic treatment, molecular testing, targeted therapy, and immune checkpoint inhibitor (ICI) use. We compared survival in the overall, treated/untreated, younger and elderly population in cohort A vs. cohort B + C (=cohort D). RESULTS: Cohorts A, B, and C included 528, 463, and 93 patients, respectively. Overall, 66% received any systemic therapy in cohort C, compared to 62% in cohort B and 55% in cohort A. Across three time periods, first-line chemotherapy rates fell (93, 76, 46%) while rates of first-line targeted therapy (5, 16, 15%) and ICI (0, 2, 36%) rose. Among molecular subtypes, first-line targeted treatment in EGFR-positive patients (63, 94, 100%) and anaplastic lymphoma kinase (ALK)-positive patients (0, 91, 100%) rose. Survival improved in all subgroups in cohort D vs. cohort A, except for patients ≥ 70 years and the untreated population. CONCLUSIONS: Systemic treatment rose across three time periods, reflecting the introduction of rapid diagnostic pathways, reflex molecular testing, ICI, and targeted therapies. Survival outcomes of advanced NSCLC patients have significantly improved.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".