Prognostication in Kidney Cancer: Recent Advances and Future Directions
Bibliographic record
Abstract
The most common type of cancer originating in the kidney is renal cell carcinoma (RCC). In both localized and advanced RCC, a number of clinical, pathologic, and molecular factors have been identified as having prognostic significance. In localized disease, risk stratification has traditionally involved the anatomic extent of disease, and several integrated scoring systems have been developed to help predict outcomes after definitive local therapy. In metastatic RCC, integrated prognostic models have also been established. These are used to stratify patients in contemporary clinical trials and to guide risk-directed treatment selection in clinical practice. Although many prognostic factors are common to both localized and advanced disease, there are some important distinctions. In both of these types of disease, the prognostic role of specific molecular and genomic alterations is an area of active investigation. In this review, we highlight the current staging systems and prognostic factors in localized and metastatic RCC. We also explore future directions in this area, including the expanding role of molecular biomarkers and their integration into the traditional prognostic models.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".