Outcomes in Black and White Patients With Metastatic Renal Cell Carcinoma Treated With First-Line Tyrosine Kinase Inhibitors: Insights From Two Large Cohorts
Bibliographic record
Abstract
PURPOSE To investigate whether black race is an independent predictor of overall survival (OS) in metastatic renal cell carcinoma (mRCC). METHODS We performed a retrospective 2-cohort (International Metastatic Renal Cell Carcinoma Database Consortium [IMDC] and trial-database) study of patients with mRCC treated with first-line tyrosine kinase inhibitors (TKIs). Unmatched (UM) and matched (M) analyses accounting for imbalances in region, year of treatment, age, and sex between races were performed. Cox models adjusting for histology, number of metastatic sites, nephrectomy, and IMDC risk compared time to treatment failure (TTF; IMDC cohort), progression-free survival (PFS; trial-database cohort), and OS. RESULTS The IMDC cohort included 73 black versus 3,381 (UM) and 1,236 (M) white patients. The trial-database cohort included 21 black versus 1,040 (UM) and 431 (M) white patients. Median OS for black versus white patients was 18.5 versus 25.8 months in the IMDC group and 21.0 versus 25.6 months in the trial-database group. Differences in OS were not significant in multivariable analysis in the IMDC group (hazard ratio [HR] M , 1.0; 95% CI, 0.7 to 1.5; HR UM , 1.1; 95% CI, 0.8 to 1.4) and trial-database (HR M , 1.5; 95% CI, 0.8 to 2.7; HR UM , 1.4; 95% CI, 0.8 to 2.6) cohorts. TTF for black patients was shorter in the UM IMDC cohort (HR UM , 1.4; 95% CI, 1.1 to 1.8; P = .003), but not in the M analysis. PFS was shorter for black patients in both analyses in the trial-database cohort (HR M , 2.3; 95% CI, 1.4 to 3.9; P = .002; HR UM , 2.3; 95% CI, 1.4 to 3.9; P = .002). CONCLUSION Black patients had more IMDC risk factors and worse outcomes with TKIs versus white patients. Race was not an independent predictor of OS. Strategies to understand biologic determinants of outcomes for minority patients are needed to optimize care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".