Renal Cell Carcinoma Subtypes and Associated Renal Malignancies: A Pictorial Review—Part I
Bibliographic record
Abstract
Renal cell carcinoma (RCC) accounts for approximately 3% of all adult malignancies, with clear cell subtype representing the majority of these cases. In the United States, the number of new cases of kidney and renal pelvis cancers was 15.6 per 100,000 men and women per year. Although the incidence of RCC has been increasing for several years, the landscape of RCC has changed significantly due to the use of highly sensitive imaging modalities. The percentage of early-stage T1 Kidney cancers has increased from 43% to more than 60% over the past two decades, with a 5-year survival rate of more than 90% for these early-stage tumors.1 As diagnostic imaging plays a significant role in the detection and management of these cancers, a fundamental understanding of RCC and its various subtypes is essential for all medical imaging specialists.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".