NP-006 Immunotherapy in second-line treatment of non-small cell lung cancer
Bibliographic record
Abstract
Background and Importance The introduction of immunotherapy in the treatment of patients with non-small cell lung cancer (NSCLC), whose disease progressed after first-line treatment, was considered an important advance. Real-life use data for these drugs are essential to measure their real added value in the treatment of these patients. Aim and Objectives Our aim was to study the effectiveness of Atezolizumab (ATZ), Nivolumab (NVL) and Pembrolizumab (PMB), in the second-line treatment of NSCLC, in real clinical practice and analyze it considering the efficacy described in published clinical trials. Materials and Methods This is an observational retrospective study of patients diagnosed with locally advanced or metastatic NSCLC, treated in second-line or later until the end of August 2021, with one of the following drugs: ATZ; NVL or PMB. Effectiveness was evaluated in terms of Progression-Free Survival and Global Survival. Results Thirty-two patients treated with ATZ, 46 with NVL and 17 with PMB were included. Of the treated patients, 59.4% for ATZ, 39.1% for NVL and 100% for PMB had positive expression of PDL1 (>1%). The median progression-free survival calculated was 5.6 months for ATZ; 8.4 months for NVL and 5.0 months for PMB. The median overall survival calculated was 16.3 months for ATZ, 15.7 months for NVL and 32.6 months for PMB. Conclusions and Relevance The progression-free survival and overall survival obtained demonstrate that, when used in clinical practice, the drugs studied are effective, with results not lower than those demonstrated in clinical trials. Immunotherapy proves to be a relevant therapy in the second- line treatment of NSCLC. Reference Lancet 2016 387(10027):1540–1550; Lancet 2017 389(10066):255–265; NEJM 2015;373(2):123–35; NEJM 2015; 373:1627–39
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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 | 0.000 |
| 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) | 0.001 | 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".