Efficacy and safety of osimertinib for patients with EGFR-mutated NSCLC: a systematic review and meta-analysis of randomized controlled studies
Bibliographic record
Abstract
Osimertinib is a recently approved third-generation epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI) that selectively inhibits both EGFR-TKI-sensitizing and EGFR-T790M resistance mutations. The aim of the present meta-analysis was to investigate the efficacy and safety of osimertinib for patients with EGFR-mutated non-small-cell lung cancer (NSCLC). Databases were searched for randomized controlled studies that reported the efficacy and safety of osimertinib versus other treatments (chemotherapy, other EGFR-TKIs, etc.) in treating EGFR-mutated NSCLC. The measured effects included objective response rate (ORR), disease control rate (DCR), progression-free survival (PFS), central nervous system progression-free survival (CNS-PFS), and overall survival (OS). Additional outcome was the incidence of adverse event. Relative risk (RR) for incidence and hazard ratio (HR) for survival outcomes were pooled. Seven studies containing 3335 participants were finally included. Osimertinib tended to improve ORR and DCR (RRs >1) as compared with other treatments. Osimertinib was also a significant protective factor for PFS, CNS-PFS, and OS (HRs <1 and p < .05). Osimertinib showed similar advantages in improving tumor response and patient survival when used as first-line, second-line, and third-line/adjuvant therapy, respectively, as compared with other treatments (RRs >1 for ORR and DCR; HRs <1 for PFS, CNS-PFS, and OS). Osimertinib also had better therapeutic effects as compared with chemotherapy, other EGFR TKIs, docetaxel + bevacizumab, and placebo, respectively. The five most common adverse events with pooled incidence > 20% were diarrhea, rash, nail effects, dry skin, and stomatitis, yet the pooled incidence of serious adverse events was less than 2%. This meta-analysis suggests that osimertinib has a positive effect in disease control and survival for patients with EGFR-mutated NSCLC with acceptable toxicities.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| 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".