Examining the evolving landscape of kidney cancer mortality in the United States
Bibliographic record
Abstract
BACKGROUND: Kidney cancer (KC) remains a significant contributor to cancer-related mortality in the United States, with an alarming increase in disease burden. To address this critical health issue, this study aims to investigate trends in KC mortality. METHODS: We retrieved mortality data from the Center for Disease Control WONDER database using the International Classification of Diseases 10 code C64. Age Standardized Mortality Rates (ASMRs) per 100 000 population were divided by age/gender/race/ethnicity-American Indian or Alaskan Native (AIAN)/Asian/African American (AA)/White/Hispanic/non-Hispanic-from 1999 to 2020. Joinpoint regression is conducted to calculate Average Annual Percentage Changes (AAPCs) and compare trends. RESULTS: A total of 284 224 deaths were reported. In 2020, the greatest ASMR was in Whites (3.9/100.000), followed by AIANs (3.5), AA (3.3), and Asians (1.6). ASMRs were 3.2 for Hispanics and 3.5 for non-Hispanics, with decreases of 11.4% and 12.5%; 5.0 for males and 2.1 for females, with decreases of 13.8% and 22.2%, respectively. AIAN males experienced the greatest ASMR decrease (44.3%), White males the smallest (1.7%). AIAN (AAPC = -1.9%), and AA (AAPC = -1.3%) showed a single negative trend line, while ASMRs in Asian (AAPC = -0.6%) and White population (AAPC = -0.6%) initially increased then declined. Younger populations experienced greater decreases, whereas populations over 85 had increasing ASMRs. CONCLUSIONS: Over 20 years, the greatest ASMR shifted from AIAN to White individuals, with a nationally decreasing trend. The elderly and male populations continue to experience greater ASMRs. Overall, our findings provide key insights for identifying at-risk populations, guiding the development of targeted strategies to reduce disparities.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".