Race/ethnicity and advanced stage of renal cell carcinoma in adults: results from surveillance, epidemiology, and end results program 2007–2015
Bibliographic record
Abstract
Non-Hispanic Blacks were shown to have an earlier stage of renal cell carcinoma (RCC) at diagnosis compared to non-Hispanic Whites. It is less clear whether disparities in RCC staging occurs for other minority races/ethnicities. We aimed to assess the association between racial/ethnic minorities and stage at diagnosis of RCC, and test for potential effect modification by histological subtype. Sourced from the Surveillance, Epidemiology and End Results (SEER) database, patients ≥20 years diagnosed with RCC from 2007 to 2015 were included (n = 37 493). Logistic regression analyses were performed to assess the independent association between race/ethnicity [non-Hispanic White, non-Hispanic Black, non-Hispanic Asian Pacific Islander, non-Hispanic American Indian/Alaskan Native (AI/AN) and Hispanic] and advanced RCC stage at diagnosis (i.e. regional spread or distant metastasis). Interaction terms were tested and stratified regression was performed accordingly. Twenty-eight percent of patients had advanced RCC stage at diagnosis. After adjusting for age, gender, year of diagnosis, histological subtype and insurance status, compared to non-Hispanic Whites, non-Hispanic Blacks had lower odds of advanced stage at diagnosis [odds ratio (OR) = 0.79; 95% confidence interval (CI) = 0.72–0.87 for clear cell; OR = 0.48; CI = 0.30–0.78 for chromophobe and OR = 0.26; CI = 0.10–0.35 for other subtypes]. Higher odds of advanced stage at diagnosis were found for non-Hispanic AI/AN in clear cell (OR = 1.27; CI = 1.04–1.55) and for Hispanics in papillary subtypes (OR = 1.58; CI = 1.07–2.33). Racial disparities in the RCC stage at diagnosis varied according to histological subtype. Further investigation on the racial disparities reported is warranted to optimize detection and ultimately improve the prognosis of patients with RCC.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".