Current Issues in the Management of Sporadic Non-clear Cell Renal Cell Carcinoma (Non-ccRCC)
Bibliographic record
Abstract
Renal cell carcinoma (RCC) is the 10th most common cancer type in Canada. Numerous developments in the management of RCC over the last decade have led to improved outcomes, though these have mostly focused on the ~80% of patients with clear cell renal cell carcinoma (ccRCC). The remaining 20% of cases are labelled non-clear cell renal cell carcinoma (non‑ccRCC) and represent a biologically and clinically heterogeneous group of diseases that are rare entities. Historically, non-ccRCC has been managed similarly to clear cell tumours. Localized non-ccRCC has better outcomes than ccRCC; however, survival of metastatic non-ccRCC is inferior to ccRCC (median overall survival [OS] of metastatic non-ccRCC reported as 39.2 months compared to 81.1 months for ccRCC). This has led to interest within the RCC scientific and patient communities to further improve outcomes for patients with non-ccRCC. This article describes the current management of patients with non-ccRCC and discusses future areas of interest in the field.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| 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".