Contemporary Management of Renal Cell Carcinoma: A Review for General Practitioners in Oncology
Bibliographic record
Abstract
Renal cell carcinoma accounts for a significant proportion of cancer diagnoses in Canadians. Over the past several years, the management of renal cell cancers has undergone rapid changes in all prognostic risk categories, resulting in improved oncologic outcomes. Novel strategies for metastatic disease make use of the synergy between checkpoints and angiogenesis inhibition. Moreover, combination checkpoint inhibition has demonstrated durable efficacy in some patients. Adjuvant immunotherapy has recently shown a survival benefit for the first time in select cases. Significant efforts are underway to explore new compounds or combinations for later-line diseases, such as inhibitors of hypoxia-inducible factors and radiolabeled biomolecules targeting tumor antigens within the neoplastic microenvironment for precise payload delivery. In this manuscript, we provide a comprehensive review of the available data addressing key therapeutic areas pertaining to systemic therapy for metastatic and localized disease, review the most relevant prognostic tools, describe local therapies and management of CNS disease, and discuss practice-changing trials currently underway. Finally, we focus on some of the practical aspects for general practitioners in oncology caring for patients with renal cell carcinoma.
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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".