4CPS-120 Erlotinib in non-small-cell lung cancer without epidermal growth factor receptor activating mutations
Bibliographic record
Abstract
Background Erlotinib is indicated in patients without epidermal growth factor receptor (EGFR)-activating mutations who have had at least one previous chemotherapy treatment that has failed and when other treatments are considered unsuitable. Purpose To analyse the effectiveness and safety of erlotinib in non-small-cell lung cancer (NSCLC) in patients without EGFR-activating mutations Material and methods A retrospective observational study was conducted in a third-level hospital. We included patients treated with erlotinib without EGFR-activating mutations from August 2012 to August 2018. Following variables were recorded: age, sex, ECOG, histopathology, progression-free survival (PFS), smokers/non-smokers or ex-smokers, type of previous chemotherapy regimens, reported adverse events (AEs) and dose reductions between cycles. We obtained data from electronic clinical records, the software where we register chemotherapy treatments (chemotherapy management software Oncogest) and the optimised computerised order entry ATHOS software. AEs were classified according to the National Cancer Institute of Canada Common Toxicity Criteria v4.0. Results Thirty-seven patients were included, with a median age of 64 years and 70% men. Fifty-seven per cent7 of patients presented ECOG 0 and the rest ECOG 1–2. Seventy-eight per cent of patients were smokers and/or ex-smokers. Ninety per centof patients received erlotinib as the second line of treatment or subsequently, and the median PFS was 9.3 weeks. Previous chemotherapy regimens used before erlotinib in NSCLC were: taxane-based-chemotherapy 60%, platinum-based-chemotherapy 83% and both 74%. Forty-eight per cent of patients had at least one AE during treatment. The most frequent was skin rash g1–2 (60%). Thirty per cent of patients had dose reductions due to toxicity. Conclusion In our patients, erlotinib median PFS was lower than in the BR21 trial. It could be explained because our patients received more previous regimens of chemotherapy for metastatic disease as well as our sample size was smaller. Regarding safety, erlotinib was well tolerated and in most of the cases, the AEs did not force a dose reduction. References and/or acknowledgements No conflict of interest.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 | 0.001 |
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".