The emerging landscape of neo/adjuvant immunotherapy in renal cell carcinoma
Bibliographic record
Abstract
Adjuvant and neoadjuvant therapies that reduce the risk of renal cell carcinoma (RCC) recurrence remain an area of unmet need. Advances have been made in metastatic RCC recently by leveraging PD-1/PD-L1 immune checkpoint inhibitors (ICIs). These agents are currently being investigated in the adjuvant and neoadjuvant settings to determine if intervention early in the disease trajectory offers a clinically meaningful benefit. While a disease-free survival benefit has been demonstrated with pembrolizumab, results from other ICI studies have not been positive to date. More mature data from these studies are needed to determine whether there is a survival benefit to ICIs in the curative-intent setting. The success of ICIs has also ushered a new wave of studies combining ICIs with other agents such as targeted therapies and vaccines, which are in early stages of investigation. We review the current state of adjuvant/neoadjuvant therapy in RCC and highlight opportunities for ongoing study.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".