Race affects access to nephrectomy but not survival in renal cell carcinoma
Bibliographic record
Abstract
OBJECTIVES: To assess whether, in contemporary patients with renal cell carcinoma (RCC), access to nephrectomy is the same between the Blacks and Whites, and that there is no difference in mortality after stratification for treatment type. PATIENTS AND METHODS: The effect of race has received little attention in RCC; only two reports have addressed and suggested the presence of racial disparities, including access to nephrectomy and survival after nephrectomy, where Black patients were disadvantaged relative to Whites. We used the Surveillance, Epidemiology and End Results data from 12 516 patients of all stages diagnosed and treated for RCC between 2000 and 2004. The effect of race (Black vs White) on nephrectomy rate was addressed in logistic regression and binomial regression models, and Cox regression models tested the effect of race on overall survival. RESULTS: Black patients were 50% less likely to have a nephrectomy than their White counterparts. However, race had no effect on overall survival when the entire cohort was assessed, as well as in subgroups of patients with or without nephrectomy. CONCLUSIONS: Although race is a determinant of access to nephrectomy, it should not be interpreted as a barrier to care, as survival was unaffected by race in patients having a nephrectomy or not. Instead, race might represent a proxy of comorbidity and life-expectancy, which represent surgical selection criteria for nephrectomy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".