Clinical outcomes according to ethnicity in patients with metastatic renal cell carcinoma (mRCC) treated with VEGF-targeted therapy (TT).
Bibliographic record
Abstract
e16065 Background: Discrepancies in clinical outcomes between different ethnic groups are well known in cancer patients. Differences in mRCC patients receiving VEGF-TT are less well characterized. We thought to report on baseline characteristics and treatment outcomes in African-Americans (AA) and Hispanic patients from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC). Methods: Caucasians, AA and Hispanics with mRCC treated with 1stline VEGF-TT were identified from the IMDC. We created 2 matched cohorts: 1) AA vs. Caucasians and 2) Hispanics vs. Caucasians, both matched for age (<50; 50-59; 60-69; <70-year-old), gender, years of treatment (2003-07; 2008-12; 2013-16) and geography (Canada, USA, Europe). Weighted Cox and logistic regressions were used to compare OS, time-to-treatment failure (TTF) and best response, adjusted for nephrectomy status, IMDC risk groups, number of metastatic sites (1 v. >1) and histology (clear-cell vs. else). Results: 73 AA and 71 Hispanics met eligibility criteria and were matched with 1236 and 901 eligible Caucasians, respectively. AA had more non-clear cell histology (26% v. 11%), time from diagnosis to therapy<1 year (67% v. 55%) and anemia (75% v. 54%) vs. Caucasians. Differences were not significant for Hispanics. Clinical outcomes are presented in Table. Conclusions: Adjusted for clinical prognostic factors, Hispanics with mRCC have statistically shorter TTF and survival than Caucasians. AA had a trend toward shorter TTF (not significant) but similar survival than Caucasians. Underlying genetic/biological differences, along with potential cultural variations, may impact survival in Hispanic mRCC patients. [Table: see text]
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".