Effect of race/ethnicity on survival in surgically treated intermediate/high risk non‐metastatic clear cell renal carcinoma
Bibliographic record
Abstract
PURPOSE: It is unknown to what extent 10-year overall survival of radical nephrectomy treated intermediate/high-risk non-metastatic clear cell renal carcinoma patients differs from age- and sex-matched population-based controls, especially when race/ethnicity is considered (Caucasian vs. African American vs. Hispanic vs. Asian/Pacific Islander). METHODS: We relied on the SEER database (2004-2018) to identify newly diagnosed radical nephrectomy treated intermediate/high risk non-metastatic clear cell renal carcinoma patients. For each case, we simulated an age- and sex-matched control relying on Social Security Administration Life Tables with 10 years of follow-up. We compared overall survival between renal carcinoma cases and population-based controls. Multivariable competing risks regression models tested for predictors of cancer-specific mortality versus other-cause mortality. RESULTS: Of 6877 radical nephrectomy treated intermediate/high risk non-metastatic clear cell renal carcinoma patients, 5050 (73%) were Caucasian versus 433 (6%) African American versus 1002 (15%) Hispanic versus 392 (6%) Asian/Pacific Islanders. At 10 years, overall survival difference between radical nephrectomy treated intermediate/high risk non-metastatic clear cell renal carcinoma patients versus population-based controls was greatest in African Americans (51% vs. 81%, Δ = 30%), followed by Hispanics (54% vs. 80%, Δ = 26%), Asian/Pacific Islanders (56% vs. 80%, Δ = 24%) and Caucasians (52% vs. 74%, Δ = 22%). In competing risks regression, only African Americans exhibited significantly higher other cause mortality (hazard ratio = 1.3; 95% confidence interval = 1.1 - 1.6; p = 0.01) than others. CONCLUSION: Relative to Life Tables' derived sex- and age-matched controls, radical nephrectomy treated intermediate/high-risk non-metastatic clear cell renal carcinoma patients exhibit worse overall survival, with worst overall survival recorded in African Americans of all race/ethnicity groups.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".