Efficacy of First-Line Immunotherapy Combined With Chemotherapy in Extensive-Stage Small Cell Lung Cancer Patients With Different Brain Metastases Status: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: This study aims to evaluate the efficacy of first-line immunotherapy combined with chemotherapy in extensive-stage small cell lung cancer (ES-SCLC) patients with differing brain metastasis statuses. Methods: We conducted a comprehensive search in public databases, such as PubMed, EMBASE, and the Cochrane Library, to identify randomized controlled trials involving ES-SCLC patients, with or without brain metastases, who underwent first-line immunotherapy combined with chemotherapy. The primary outcome measure was progression-free survival (PFS), and the secondary outcome measure was overall survival (OS). Results: Our analysis incorporated seven high-quality randomized controlled trials, encompassing 398 patients with brain metastases and 3,533 without. Among patients without brain metastases, the combination of immunotherapy and chemotherapy led to significantly improved PFS (hazard ratio (HR) = 0.72, 95% confidence interval (CI): 0.62 - 0.84, P < 0.001) and OS (HR = 0.77, 95% CI: 0.67 - 0.88, P < 0.001) in comparison to chemotherapy alone. Conversely, for patients with brain metastases, the addition of immunotherapy to chemotherapy did not result in a significant improvement in PFS (HR = 1.03, 95% CI: 0.66 - 1.61, P = 0.887) or OS (HR = 1.03, 95% CI: 0.82 - 1.31, P = 0.776) when compared to chemotherapy alone. Conclusions: In ES-SCLC patients without brain metastases, first-line immunotherapy combined with chemotherapy demonstrated improved PFS and OS in contrast to chemotherapy alone. However, patients with brain metastases did not experience similar benefits.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".