Neoadjuvant and Adjuvant Therapy for Renal Cell Carcinoma
Bibliographic record
Abstract
Patients undergoing definitive surgery or ablative techniques for nonmetastatic kidney cancer have varying degrees of risk of recurrent disease post procedure. The ultimate goal of “adjuvant therapy” is to reduce the incidence of recurrent disease, and to cure more patients. We summarize the current state of perioperative therapy for kidney cancer and explore future directions to develop optimal adjuvant strategies. We define risk and risk of recurrence post-definitive therapy, describe the controversies surrounding the trial landscape of adjuvant vascular endothelial growth factor receptor tyrosine kinase inhibitors and immune checkpoint inhibitors. We review data on neoadjuvant therapy before advanced kidney cancer resection. Radiologic, ethnic, economic, and geographic considerations with respect to adjuvant therapy are highlighted, as well as adjuvant therapy issues especially pertinent to patients, future directions in adjuvant trial design specifically targeted to biomarkers and patient selection, and sequencing of treatment after adjuvant therapy in those patients with recurrence.
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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".