04. ASSESSMENT OF EFFICACY AND SAFETY OF OSIMERTINIB FOR PATIENTS WITH INTRACRANIAL METASTATIC DISEASE: A SYSTEMATIC REVIEW AND META-ANALYSIS
Bibliographic record
Abstract
Abstract INTRODUCTION Intracranial metastatic disease (IMD) is a serious and life-altering complication for many patients with cancer. Targeted therapy may address limitations of current treatments as an additional agent to achieve intracranial disease control in some patients with IMD. Osimertinib is a mutant epidermal growth factor receptor (EGFR) inhibitor that can penetrate the blood-brain barrier and inhibit tumor cell survival and proliferation in patients with non-small cell lung cancer (NSCLC) with specific EGFR mutations. The purpose of this study is to assess the efficacy and safety of osimertinib in the management of IMD. METHODS Studies reporting intracranial outcomes for patients with EGFR-mutant NSCLC and IMD treated with osimertinib were included. Among 271 records identified in MEDLINE and EMBASE, 15 studies fulfilled eligibility criteria. Outcomes were pooled using a random-effects model. Risk of bias was assessed using the Cochrane Risk of Bias tool and modified Newcastle-Ottawa scale. Information extracted included study characteristics, intracranial efficacy measures, and safety measures. Meta-analyses were conducted to pool applicable outcomes. RESULTS 15 studies reporting on 324 patients were included in the analysis. Combined CNS ORR and CNS DCR were calculated to be 64% (95% CI, 53–76%; n = 195), and 90% (95% CI, 85–93%; n = 246). Risk ratios for CNS ORR and CNS DCR were calculated to be 1.44 (95% CI, 1.06–1.96; n = 52) and 1.13 (95% CI, 0.96–1.33; n = 52). Included studies reported complete intracranial response rates of 7–23%, median best decrease in intracranial lesion size of 40–64%, and grade 3+ adverse event rates of 19–39%. CONCLUSIONS Findings reported here support a potential role for osimertinib for patients with EGFR-mutant NSCLC and IMD. Clinical decision-makers would benefit from the inclusion of patients with IMD in future trials to identify factors that predict responses to targeted therapy.
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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 | 0.017 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.060 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".