Survival advantage of Asian metastatic prostate cancer patients treated with external beam radiotherapy over other races/ethnicities
Bibliographic record
Abstract
PURPOSE: To assess the effect of race/ethnicity in cancer-specific mortality (CSM) adjusted for other-cause mortality (OCM) in metastatic prostate cancer patients (mPCa) treated with external beam radiotherapy (EBRT) to the prostate. METHODS: We relied on the Surveillance, Epidemiology, and End Results (SEER) database to identify Caucasian, African-American, Hispanic/Latino and Asian mPCa patients treated by EBRT between 2004 and 2016. Cumulative incidence plots displayed CSM after adjustment for OCM according to race/ethnicity. Propensity score matching accounted for patient age, prostate-specific antigen, clinical T and N stages, Gleason Grade Groups and M1 substages. OCM adjusted multivariable analyses tested for differences in CSM in African-Americans, Hispanic/Latinos and Asians relative to Cauacasians. RESULTS: After 3:1 propensity score matching and OCM adjustment, Asians exhibited lower CSM at 60 and 120 months (48.2 and 60.0%, respectively) compared to Caucasians (66.7 and 79.4%, respectively, p < 0.001). In OCM adjusted multivariable analyses, Asian race/ethnicity was associated with lower CSM (HR 0.66, CI 0.52-0.83, p < 0.001). Conversely, African-American and Hispanic/Latino race/ethnicity did not affect CSM. OCM rates were comparable between examined races/ethnicities. CONCLUSION: In the setting of mPCa treated with EBRT, Asians exhibit lower CSM than Caucasians, African-Americans and Hispanic/Latinos. This observation may warrant consideration in prognostic stratification schemes for newly diagnosed mPCa patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".