Chemo-Immunotherapy in First Line Extensive Stage Small Cell Lung Cancer (ES-SCLC): A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Small cell lung cancer (SCLC) is an aggressive neuroendocrine carcinoma with early metastatic potential. The standard-of-care treatment has not changed in years. Recent studies report improved progression-free survival (PFS) and overall survival (OS) with combined ICI and chemotherapy in ES-SCLC. We conducted a systematic review and meta-analysis to assess the magnitude of survival benefits. We searched MEDLINE, EMBASE, and Cochrane between 1 January 2010 and 15 July 2022 and conference proceedings from 2018 to 2022, for randomised controlled trials, evaluating chemo-ICI compared with platinum-doublet chemotherapy in untreated ES-SCLC. Outcomes assessed were PFS, OS, objective response rate (ORR), duration of response (DoR), toxicity, and health-related quality of life (HRQoL). The search identified 8061 studies, with 8 (56 publications) included in the final analysis. PFS and OS were significantly improved for patients randomised to chemo-ICI (PFS hazard ratio (HR) 0.75, 95% confidence interval (CI) 0.70-0.80) and (OS HR 0.79, 95% CI 0.73-0.85). Subgroup analysis demonstrated a differential effect between PD-1/PD-L1 and CTLA-4 inhibitors. There was no difference in ORR and DoR. All-grade adverse events (RR 1.06, 95% CI 1.00-1.12) were similar. The addition of ICI to chemotherapy in untreated ES-SCLC results in a 22% risk reduction in death, and a 25% risk reduction in disease progression with a minimal increase in toxicity. These improvements are modest but represent progress beyond the standard of 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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.032 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".