Novel active agents in patients with advanced NSCLC without driver mutations who have progressed after first-line chemotherapy
Bibliographic record
Abstract
Despite the efficacy of a number of first-line treatments, most patients with advanced-stage non-small cell lung cancer (NSCLC) experience disease progression that warrants further treatment. In this review, we examine the role of novel active agents for patients who progress after first-line therapy and who are not candidates for targeted therapies. More therapeutic options are needed for the management of patients with NSCLC after failure of first-line chemotherapy. A PubMed search was performed for articles from January 2012 to May 2015 using the keywords NSCLC, antiangiogenic, immunotherapy, second-line, novel therapies and English language articles only. Relevant papers were reviewed; papers outside that period were considered on a case-by-case basis. A search of oncology congresses was performed to identify relevant abstracts over this period. In recent years, antiangiogenic agents and immune checkpoint inhibitors have been added to our armamentarium to treat patients with advanced NSCLC who have progressed on first-line chemotherapy. These include nintedanib, a triple angiokinase inhibitor; ramucirumab, a vascular endothelial growth factor receptor-2 antibody; and nivolumab, pembrolizumab and atezolizumab, just three of a growing list of antibodies targeting the programmed death receptor-1 (PD-1)/PD ligand-1 pathway. Predictive and prognostic factors in NSCLC treatment will help to optimise treatment with these novel agents. The approval of new treatments for patients with NSCLC after the failure of first-line chemotherapy has increased options after a decade of few advances, and holds promise for future evolution of the management of NSCLC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".