Treatment and outcomes of limited stage small cell lung cancer in the Canadian small cell lung cancer database (CASCADE)
Bibliographic record
Abstract
INTRODUCTION: There have been minimal advances in the systemic treatment of limited stage small cell lung cancer (LS-SCLC) for decades. With the publication of the ADRIATIC trial, consolidation durvalumab is a new standard of care. This study evaluated real-world treatments and clinical outcomes prior to the era of immunotherapy for LS-SCLC. METHODS: The Canadian Small Cell Lung Cancer Database (CASCADE) is a Canadian multi-institutional federated database that includes patients with SCLC from 9 academic institutions. This analysis included patients with pathologically confirmed LS-SCLC treated curatively between January 2001 and December 2022. Baseline characteristics and treatment patterns were obtained from medical records and assessed descriptively. The primary outcome was overall survival (OS) assessed using Kaplan Meier (KM) methods. OS was also assessed among patients who received treatment eligible for participation in the ADRIATIC trial. RESULTS: A total of 1,024 patients were included. Median age was 66 years old and 52 % of patients were female. Concurrent chemoradiation therapy (cCRT) was the most common treatment modality (76 %), followed by surgery (11 %) and sequential CRT (10 %). Median OS was 24.9 months (95 % confidence interval [CI] = 23.4-27.4). Only 36 % of patients treated with cCRT received treatment meeting eligibility criteria for the ADRIATIC trial. The most common reason for ineligibility was due to the radiation therapy (RT) dose/schedule used. Median OS of eligible and ineligible patients was 30.3 months (95 % CI = 26.4-36.4) versus 21.7 months (95 % CI = 19.7-24.9). CONCLUSIONS: Survival outcomes for LS-SCLC remain poor despite curative-intent multimodality treatment. Immunotherapy represents a promising advance but a high proportion of patients in the real-world receive treatment that was not represented in the ADRIATIC trial. Future work evaluating the outcomes of patients treated with immunotherapy is critical to assess its real-world impact.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".