Association between treatment and improvements in overall survival of patients with advanced/metastatic non–small cell lung cancer since 2011: A study in the United States, Canada, and Germany using retrospective real‐world databases
Bibliographic record
Abstract
BACKGROUND: This study aimed to describe treatment patterns and overall survival (OS) in patients with advanced non-small cell lung cancer (aNSCLC) in three countries between 2011 and 2020. METHODS: Three databases (US, Canada, Germany) were used to identify incident aNSCLC patients. OS was assessed from the date of incident aNSCLC diagnosis and, for patients who received at least a first line of therapy (1LOT), from the date of 1LOT initiation. In multivariable analyses, we analyzed the influence of index year and type of prescribed treatment on OS. FINDINGS: We included 51,318 patients with an incident aNSCLC diagnosis. The percentage of patients treated with a 1LOT differed substantially between countries, whereas the number of patients receiving immunotherapies/targeted treatments increased over time in all three countries. Median OS from the date of incident diagnosis was 9.9 months in the United States vs. 4.1 months in Canada. When measured from the start of 1LOT, patients had a median OS of 10.7 months in the United States, 10.9 months in Canada, and 10.9 months in Germany. OS from the start of 1LOT improved in all three countries from 2011 to 2020 by approximately 3 to 4 months. CONCLUSIONS: Observed continuous improvement in OS among patients receiving at least a 1LOT from 2011 to 2020 was likely driven by improved care and changes in the treatment landscape. The difference in the proportion of patients receiving a 1LOT in the observed countries requires further investigation.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".