Chronic Kidney Disease and Risk of Renal Cell Carcinoma
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it