The effect of race on survival after local therapy in metastatic prostate cancer patients
Bibliographic record
Abstract
INTRODUCTION: Local therapy (LT) may offer a survival advantage in highly select, newly diagnosed metastatic prostate cancer (mPCa) patients. However, it is unknown whether the benefits vary in Caucasian vs. African American (AA) patients. METHODS: Within the Surveillance Epidemiology and End Results (SEER) database (2004-2014), we focused on Caucasians and AA patients with newly diagnosed mPCa treated with LT: radical prostatectomy (RP) and brachytherapy (RT). Endpoints consisted of cancer-specific mortality (CSM) and overall mortality (OM). Kaplan-Meier analyses and multivariable Cox regression models tested for racial difference in CSM and OM. RESULTS: Between 2004 and 2014, we identified 408 (77.2%) Caucasians and 121 (22.8%) AAs with newly diagnosed mPCa treated with LT: RP (n=357) or RT (n=172). According to race, when LT is defined as RP, Caucasian patients had a significantly longer survival vs. AA patients: CSM-free survival 123 vs. 63 months (p=0.004) and OM-free survival 108 vs. 46 months (p=0.002). The CSM and OM benefits were confirmed in multivariable analyses (hazard ratio [HR] 0.56, p=0.01 for CSM; HR 0.60, p=0.01 for OM). However, no differences in CSM or OM were recorded according to race when LT consisted of RT. CONCLUSIONS: Our results indicate that race is not associated with difference in survival after LT in mPCa patients. However, when focusing on RP-treated patients, Caucasian race is associated with higher CSM and OM rates relative to AA race. This racial difference does not apply to RT. Our findings should be considered in future prospective trials for the purpose of preplanned stratification according to race.
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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 | 0.003 |
| 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".