Real‐world survival outcomes of immunotherapy for advanced non‐small cell lung cancer: A single‐center retrospective review
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer (NSCLC) is often diagnosed at an advanced stage. Clinical trials have demonstrated that first-line immunotherapy alone or in combination with chemotherapy improves overall survival. However, reports of survival outcomes in real-world settings are limited. We assessed survival in advanced NSCLC patients treated with immunotherapy alone or in combination with chemotherapy in first- or second-line at the Windsor Regional Cancer Program (WRCP) and compared it to existing literature. METHODS: We included patients diagnosed with stage IV NSCLC from January 2015 to December 2020 and treated with first-line chemoimmunotherapy (ChemoImmuno1), chemotherapy followed by immunotherapy (Chemo1), or immunotherapy followed by chemotherapy (Immno1) in our survival analysis. Patients with oncogene-addicted mutations were excluded. RESULTS: There were 160 patients of which 41.5% were female. Mean age was 68 years. Median overall survival from time of diagnosis was 474 days (95% CI: 249, 949) with an estimated 5-year survival of 11.1% (95% CI: 4.5, 21.3). Median OS in ChemoImmuno1 was 9.6 months, in Chemo1 was 19.2 months from time of diagnosis and 10.5 months from time of initiation of immunotherapy, and in Immuno1 was 18.4 months, respectively. Estimated survival at three years from time of diagnosis for ChemoImmuno1 was 17.6% and for Immuno1 was 17.9%. For Chemo1, from diagnosis it was 20.1% and from second-line therapy it was 15.4%. Survival outcomes were comparable to clinical trials and other studies. CONCLUSION: Real-world survival outcomes of immunotherapy for advanced NSCLC are comparable to the existing literature in this single center study.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".