Real-world treatment patterns and outcomes for patients with non-metastatic non-small cell lung cancer: retrospective analyses in Canada, England, and Germany
Bibliographic record
Abstract
BACKGROUND: Recent therapeutic advancements for non-metastatic non-small cell lung cancer (NSCLC) have increased the need for real-world baselines against which future changes in patient management and clinical outcomes can be compared. METHODS: Data on patient characteristics, initial treatment, and overall survival (OS) were derived from adult patients diagnosed with stage I-IIIC NSCLC (2010-2020) in a regional Canadian database (Oncology Outcomes [O2]), an English national registry (Cancer Analysis System [CAS]), and four regional German registries (VONKOdb) and retrospectively analyzed separately using analogous methodology. RESULTS: Data from 85,433 patients were analyzed. Stage at diagnosis varied, with proportions with stage I NSCLC ranging from 30.9% (VONKOdb) to 44.2% (O2) and with stage III disease from 36.9% (O2) to 48.5% (VONKOdb). Across the data sources, proportions receiving surgery ± other treatments were similar for stages I and II, but decreased through stages IIIA, IIIB, and IIIC (range, 24.7-42.7%, 4.6-21.8%, and 0.9-7.5%, respectively). Overall, 70.3-85.2% of patients received active treatment for NSCLC, with a trend toward lower proportions among those with stage III disease. Reached median OS tended to be longest in patients with resected stage I/II NSCLC (range, 28.8-128.0 months) and shortest in patients with stage IIIB/IIIC disease treated with systemic anticancer therapy (SACT) alone, radiotherapy alone, or SACT + palliative radiotherapy (range, 4.8-21.2 months). CONCLUSIONS: These data provide insights into treatment pathways and survival outcomes before the widespread use of immunotherapy-based and targeted therapies and will serve as an important baseline for future evaluations of emerging treatments for patients with non-metastatic NSCLC.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".