Does CNS metastases reduce systemic therapy lines for NSCLC patients with EGFR mutation?
Bibliographic record
Abstract
e21558 Background: The latest generation tyrosine kinase inhibitor (TKI), Osimertinib, targets the epidermal growth factor receptor (EGFR) despite the T790M mutation status in non-small-cell lung cancer (NSCLC). In cases where there is a detected EGFR mutation on the exon 19-deletion and on the exon 21-L858R in the NSCLC population, studies have demonstrated that Osimertinib has a positive benefit in overall survival and delayed progression of central nervous system (CNS) metastases. Methods: From January 2010 to December 2018, 56 patients with the metastatic NSCLC-EGFR mutation, treated with Osimertinib 80 mg once daily, were included in this analysis. Retrospective data was extracted through the internal administrative databases located at Sunnybrook Hospital. All patients had EGFR mutation positivity by cytology, plasma or tissue sampling. The primary endpoint was to evaluate whether NSCLC patients who were exposed to Osimertinib and had brain metastases underwent fewer systemic therapy lines as compared to those who did not have metastases involving the brain. Results: Eligible patients were analyzed and the median age at the initial diagnosis was 65 years old; 50% (n = 28) of the patients had brain metastases. The median of systemic treatment lines for patients without CNS metastasis was two and for those who have metastases to the brain was three. 82,2% of this cohort received Osimertinib in 2nd line, after development of acquired resistance to first or second TKI generation. Conclusions: Results from this study did not demonstrate that EGFR mutated, NSCLC patients with CNS metastases received less systemic therapy lines to those without metastases involving the brain. A larger cohort for further investigation is warranted.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".