Management of Metastatic Clear Cell Renal Cell Carcinoma: ASCO Guideline
Bibliographic record
Abstract
PURPOSE: To provide recommendations for the management of patients with metastatic clear cell renal cell carcinoma (ccRCC). METHODS: An Expert Panel conducted a systematic literature review to obtain evidence to guide treatment recommendations. RESULTS: The panel considered peer-reviewed reports published in English. RECOMMENDATIONS: The diagnosis of metastatic ccRCC should be made using tissue biopsy of the primary tumor or a metastatic site with the inclusion of markers and/or stains to support the diagnosis. The International Metastatic RCC Database Consortium risk criteria should be used to inform treatment. Cytoreductive nephrectomy may be offered to select patients with kidney-in-place and favorable- or intermediate-risk disease. For those who have already had a nephrectomy, an initial period of active surveillance may be offered if they are asymptomatic with a low burden of disease. Patients with favorable-risk disease who need systemic therapy may be offered an immune checkpoint inhibitor (ICI) in combination with a vascular endothelial growth factor receptor (VEGFR) tyrosine kinase inhibitor (TKI); patients with intermediate or poor risk should be offered a doublet regimen (no recommendation was provided between ICIs or an ICI in combination with a VEGFR TKI). For select patients, monotherapy with either an ICI or a VEGFR TKI may be offered on the basis of comorbidities. Interleukin-2 remains an option, although selection criteria could not be identified. Recommendations are also provided for second- and subsequent-line therapy as well as the treatment of bone metastases, brain metastases, or the presence of sarcomatoid features. Participation in clinical trials is highly encouraged for patients with metastatic ccRCC.Additional information is available at www.asco.org/genitourinary-cancer-guidelines.
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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".