Comparison of outcomes for Hispanic and non‐Hispanic patients with advanced renal cell carcinoma in the International Metastatic Renal Cell Carcinoma Database
Bibliographic record
Abstract
BACKGROUND: Existing data on the impact of Hispanic ethnicity on outcomes for patients with renal cell carcinoma (RCC) is mixed. The authors investigated outcomes of Hispanic and non-Hispanic White (NHW) patients with advanced RCC receiving systemic therapy at large academic cancer centers using the International Metastatic Renal Cell Carcinoma Database (IMDC). METHODS: Eligible patients included non-Black Hispanic and NHW patients with locally advanced or metastatic RCC initiating systemic therapy. Overall survival (OS) and time to first-line treatment failure (TTF) were calculated using the Kaplan-Meier method. The effect of ethnicity on OS and TTF were estimated by Cox regression hazard ratios (HRs). RESULTS: A total of 1563 patients (181 Hispanic and 1382 NHW) (mostly males [73.8%] with clear cell RCC [81.5%] treated with tyrosine kinase inhibitor [TKI] monotherapy [69.9%]) were included. IMDC risk groups were similar between groups. Hispanic patients were younger at initial diagnosis (median 57 vs. 59 years, p = .015) and less likely to have greater than one metastatic site (60.8% vs. 76.8%, p < .001) or bone metastases (23.8% vs. 33.4%, p = .009). Median OS and TTF was 38.0 months (95% confidence interval [CI], 28.1-59.2) versus 35.7 months (95% CI, 31.9-39.2) and 7.8 months (95% CI, 6.2-9.0) versus 7.5 months (95% CI, 6.9-8.1), respectively, in Hispanic versus NHW patients. In multivariable Cox regression analysis, no statistically significant differences were observed in OS (adjusted hazard ratio [HR], 1.07; 95% CI, 0.86-1.31, p = .56) or TTF (adjusted HR, 1.06; 95% CI, 0.89-1.26, p = .50). CONCLUSIONS: The authors did not observe statistically significant differences in OS or TTF between Hispanic and NHW patients with advanced RCC. Receiving treatment at tertiary cancer centers may mitigate observed disparities in cancer outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".