Role of immunotherapy in kidney cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize current knowledge on promising immunotherapeutic agents and to provide a brief outline of current use of immunotherapeutic agents in patients with locally advanced or metastatic renal cell carcinoma (RCC). RECENT FINDINGS: Immunotherapy with mAbs directed against programed death cell protein 1, programed death-ligand 1 (PD-L1) and cytotoxic T-Lymphocyte Antigen 4 has become new first-line standard of care for moderate and poor-risk metastatic RCC patients. Similarly, the combination immune-oncology treatment and vascular endothelial growth factor (VEGF) mAbs also showed promising results in first-line therapy despite relative data immaturity. Finally, immune-oncology monotherapy (nivolumab) already represents second or third-line standard of care after tyrosine kinase inhibitor failure. SUMMARY: Combination immune-oncology therapy represents the standard of care for management of intermediate-to-poor risk clear cell metastatic RCC. In addition, combination of immune-oncology and anti-VEGF antibody represents a treatment option across all risk levels in patient with elevated PD-L1 expression. Finally, nivolumab is one of two ideal treatment options in second-line clear cell metastatic RCC patients.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
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".