Comparative Efficacy and Safety of Immunotherapeutic Regimens with PD-1/PD-L1 Inhibitors for Previously Untreated Extensive-Stage Small Cell Lung Cancer: A Systematic Review and Network Meta-Analysis
Bibliographic record
Abstract
Improving therapeutic strategies for extensive-stage small cell lung cancer (ES-SCLC) remains a challenge. To date, no reports have directly compared the efficacy and safety of immune checkpoint inhibitors plus platinum-etoposide (ICIs+EP) with platinum-irinotecan (IP) or directly compared different ICIs+EP for previously untreated ES-SCLC. This study used a Bayesian approach for network meta-analysis to compare efficacy and safety between ICIs+EP and IP and between each pair of three ICIs+EP. The six treatment arms were: pembrolizumab plus platinum-etoposide (Pem+EP), durvalumab plus platinum-etoposide (Dur+EP), atezolizumab plus platinum-etoposide (Atz+EP), platinum-amrubicin (AP), IP, and platinum-etoposide (EP). No significant differences in overall survival were observed between ICIs+EP and IP and between each pair of three ICIs+EP. The incidence of ≥grade 3 adverse events (G3-AEs) was significantly higher in ICIs+EP than IP, whereas no significant difference was found in G3-AEs between each pair of three ICIs+EP. The incidence of ≥grade 3 neutropenia and thrombocytopenia was significantly higher in ICIs+EP than IP, whereas the incidence of ≥grade 3 diarrhea was significantly lower in ICIs+EP than IP. These findings will help clinicians better select treatment strategies for ES-SCLC.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".