A Population-based Study of Treatment Patterns and Survival of Patients With De Novo Stage IV Non–Small Cell Lung Cancer
Bibliographic record
Abstract
BACKGROUND: Treatment strategies for metastatic non-small cell lung cancer (NSCLC) are evolving rapidly and can be highly variable. Real-world evidence of treatment patterns and outcomes can provide an understanding of our current practice and offer insights on ways to incorporate emerging therapies into our treatment paradigm. In this population-based study, we investigated treatments and outcomes of stage IV NSCLC patients from a large Canadian province. METHODS: Patients diagnosed with de novo stage IV NSCLC from April 1, 2010 to March 31, 2015 were identified. Data for baseline characteristics, treatments, and outcomes were obtained from provincial data sources, including the cancer registry and electronic medical records. We classified systemic treatments as chemotherapy, targeted therapy (anti-epidermal growth factor receptor, and anti-anaplastic lymphoma kinase) and immunotherapy (checkpoint inhibitors) and characterized clinical outcomes by treatment type. RESULTS: A total of 6438 patients were identified with NSCLC, of whom 3606 (56%) had de novo stage IV disease. The median age of diagnosis was 69 (range: 20 to 100) years and 52.4% were men. First-line palliative treatments included: chemotherapy in 19.5% (n=703), targeted agents in 5.7% (n=204), immunotherapy in 1% (n=1), radiotherapy in 6.8% (n=246), and best supportive care in 74.8% (n=2,698). Median overall survival (mOS) from diagnosis for the whole cohort was 3.8 months. Within subgroups, mOS was 18.0 months for targeted therapies, 9.4 months for chemotherapy, and 2.5 months for best supportive care. Only 1.0% of patients (n=34) received immunotherapy at any line. CONCLUSIONS: Survival benefit was dependent on type of treatment received, with significantly better mOS observed with the use of small-molecule targeted therapy against epidermal growth factor receptor mutations and anaplastic lymphoma kinase rearrangements, as compared with best supportive care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".