Algorithm for the Treatment of Advanced or Metastatic Squamous Non-Small-Cell Lung Cancer: An Evidence-Based Overview
Bibliographic record
Abstract
The treatment of squamous non-small-cell lung cancer (nsclc) is evolving. In the past, the backbone of treatment was chemotherapy, with very few other options available. Fortunately, that situation is changing, especially with a better understanding of tumour biology. Various strategies have been tried to improve patient outcomes. The most notable advance must be immunotherapy, which has revolutionized the treatment paradigm for lung cancer in patients without a driver mutation. Immunotherapy is now the treatment of choice in patients who have progressed after chemotherapy and is replacing chemotherapy as upfront therapy in a selected population. Other strategies have also been tried, such as the addition of targeted therapy to chemotherapy. Targeted agents include ramucirumab, an inhibitor of vascular endothelial growth factor receptor 2, and necitumumab, a monoclonal antibody against epithelial growth factor receptor. Recently, advances in molecular profiling have also been applied to tumours of squamous histology, in which multiple genetic alterations, including mutations and amplifications, have been described. Research is actively seeking targetable mutations and testing various therapies in the hopes of further improving prognosis for patients with squamous nsclc. Here, we review the various advances in the treatment of squamous nsclc and present a proposed treatment algorithm based on current evidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".