Two-year survival with nivolumab in previously treated advanced non–small-cell lung cancer: A real-world pooled analysis of patients from France, Germany, and Canada
Bibliographic record
Abstract
OBJECTIVES: Immune checkpoint inhibitors have become the standard of care for metastatic non-small-cell lung cancer (NSCLC) progressing during or after platinum-based chemotherapy. Real-world clinical practice tends to represent more diverse patient characteristics than randomized clinical trials. We sought to evaluate overall survival (OS) outcomes in the total study population and in key subsets of patients who received nivolumab for previously treated advanced NSCLC in real-world settings in France, Germany, or Canada. MATERIALS AND METHODS: Data were pooled from two prospective observational cohort studies, EVIDENS and ENLARGE, and a retrospective registry in Canada. Patients included in this analysis were aged ≥18 years, had stage IIIB/IV NSCLC, and received nivolumab after at least one prior line of systemic therapy. OS was estimated in the pooled population and in various subgroups using the Kaplan-Meier method. Timing of data collection varied across cohorts (2015-2019). RESULTS: Of the 2585 patients included in this analyses, 1235 (47.8 %) were treated in France, 881 (34.1 %) in Germany, and 469 (18.1 %) in Canada. Median OS for the total study population was 11.3 months (95 % CI: 10.5-12.2); this was similar across France, Germany, and Canada. The OS rate was 49 % at 1 year and 28 % at 2 years for the total study population. In univariable Cox analyses, the presence of epidermal growth factor receptor mutations in nonsquamous disease, liver, or bone metastases were associated with significantly shorter OS, whereas tumor programmed death ligand 1 expression and Eastern Cooperative Oncology Group performance status 0-1 were associated with significantly prolonged OS. Similar OS was noted across subgroups of age and prior lines of therapy. CONCLUSION: OS rates in patients receiving nivolumab for previously treated advanced NSCLC in real-world clinical practice closely mirrored those in phase 3 studies, suggesting similar effectiveness of nivolumab in clinical trials and clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".