Antiangiogenic Therapies in Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Angiogenesis is frequent in non-small-cell lung cancer (nsclc) and is associated with more aggressive disease. Many clinical trials have evaluated the addition of antiangiogenic therapy to standard therapies for patients with nsclc. Bevacizumab, a monoclonal antibody directed against serum vascular endothelial growth factor, in combination with carboplatin-paclitaxel chemotherapy, has been shown to improve survival for patients with nsclc. However, bevacizumab-based therapy is not suitable for many nsclc patients, including those with squamous histology, poor performance status, brain metastases, and the presence of bleeding or thrombotic disorders. Similar efficacy has also been seen with carboplatin-pemetrexed followed by maintenance pemetrexed chemotherapy. In the second-line setting, the addition of ramucirumab to docetaxel-or the addition of bevacizumab to paclitaxel-has resulted in a modest improvement in efficacy, although the clinical importance of those findings is questionable. Many trials in nsclc have also evaluated oral antiangiogenic compounds, both in the first line in combination with chemotherapy and upon disease progression either as combination or single-agent therapy. No clear improvements in overall survival have been observed, although a subgroup analysis of a trial evaluating the addition of nintedanib to docetaxel showed improved survival that was limited to patients with adenocarcinoma. Those findings require validation, however. All of the oral antiangiogenic agents result in added toxicities. Some agents have resulted in an increased risk of death, limiting their development. Available evidence supports a limited number of antiangiogenic therapies for patients with nsclc, but no biomarkers to help in patient selection are currently available, and additional translational research is needed to identify predictive biomarkers for antiangiogenic therapy.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".