Epidemiology of Renal Cell Carcinoma
Bibliographic record
Abstract
Though renal cell carcinoma (RCC) accounts for 2% of global cancer diagnoses and deaths, it has more than doubled in incidence in the developed world over the past half-century, and today is the ninth most common neoplasm in the United States (US). While North America and Western Europe have the highest disease burden (with the Belarus highest in incidence), Latin America, Asia and Africa are projected to see an increase in incidence as nation's transition to a Western lifestyle. Most cases of RCC are discovered incidentally on imaging, and survival is highly dependent on the stage at diagnosis, with the metastatic disease having only a 12% 5-year survival rate. Two-thirds of RCC diagnoses are made in men, and the average age of diagnosis in the US is 64. Those with genetic predispositions, namely von Hippel-Lindau disease, tend to be diagnosed 20 years earlier. RCC has a greater incidence among Hispanics and Native Americans, and a lower survival rate among African Americans in the US. Modifiable risk factors for RCC include smoking, obesity, poorly-controlled hypertension, diet and alcohol, and occupational exposures. Prevention strategies aimed at improving survival and reducing disparities include addressing lifestyle factors and access to regular healthcare among underserved populations and in developing nations, as well as more rigorous imaging guidelines to detect RCC at an earlier stage. A stronger understanding of global RCC epidemiology can facilitate prevention efforts, especially in developing nations and underserved communities where disease burden is predicted to rise in the coming decades.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".