Chronic Kidney Disease and Risk of Renal Cell Carcinoma
Bibliographic record
Abstract
BACKGROUND: The incidence of renal cell carcinoma in the United States differs by race/ethnicity. To better understand these disparities, we conducted a nested case-control study investigating renal cell carcinoma risk factors across racial/ethnic groups within the Kaiser Permanente Northern California health care network. METHODS: Our study included 3136 renal cell carcinoma cases (2152 whites, 293 blacks, 425 Hispanics, and 255 Asians) diagnosed between 1998 and 2008 and 31031 individually matched controls (21478 whites, 2836 blacks, 4147 Hispanics, and 2484 Asians). Risk of renal cell carcinoma was assessed in relation to smoking status, body mass index (BMI), hypertension, and chronic kidney disease. We calculated odds ratios (ORs) and 95% confidence intervals (CIs) using conditional logistic regression, and population attributable risk (PAR) to estimate by race the proportion of cases attributable to hypertension and chronic kidney disease. RESULTS: The association between chronic kidney disease and renal cell carcinoma differed markedly by race (Pinteraction < 0.001), with associations observed among blacks (OR = 10.4 [95% CI = 6.0-17.9]), Asians (5.1 [2.2-11.7]), and Hispanics (2.3 [1.1-4.6]) but not whites (1.1 [0.6-1.9]). Hypertension, high BMI, and smoking were associated with renal cell carcinoma, but findings generally did not differ by race. Relative to other racial/ethnic groups, blacks had the highest proportion of renal cell carcinoma incidence attributable to hypertension and chronic kidney disease (combined, PAR = 37%; hypertension only, PAR = 27%; chronic kidney disease, PAR = 10%). CONCLUSIONS: Our findings suggest that hypertension and chronic kidney disease likely have contributed to the observed excess in renal cell carcinoma incidence among blacks compared with whites.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".