Asian Race and Risk of Prostate Cancer: Results from the REDUCE Study
Bibliographic record
Abstract
Abstract Background: Global prostate cancer incidence rates are lower in Asian men than Caucasian men. Whether this is the result of less screening in Asian men remains to be determined. We examined whether Asian race was associated with prostate cancer diagnosis in the Reduction by Dutasteride of Cancer Events (REDUCE) study. Methods: REDUCE was a 4-year, multicenter, randomized trial of dutasteride versus placebo for prostate cancer prevention among men who underwent prostate-specific antigen (PSA)-independent biopsies at 2 and 4 years. Eligible men were ages 50 to 75 years, had PSA between 2.5 and 10 ng/mL, and a negative prestudy prostate biopsy. We tested the association between Asian and Caucasian race and prostate cancer diagnosis using logistic regression. Results: Of 8,122 men in REDUCE, 5,755 (71%) were Caucasian and 105 (1.8%) were Asian. Asians had lower body mass index (24.8 vs. 26.9 kg/m2, P < 0.001), had smaller prostate volume (35.0 vs. 43.5 cc, P < 0.001), and were less likely to have abnormal digital rectal exams (P = 0.048), but were similar in baseline age, PSA, family history of prostate cancer, and smoking status compared with Caucasian men (all P ≥ 0.164). Asian men were equally likely to receive any on-study biopsy compared with Caucasian men (P = 0.634). After adjusting for potential confounders, Asian men were less likely to be diagnosed with prostate cancer during the 4-year study (OR = 0.49; 95% confidence interval, 0.28–0.88; P = 0.016), compared with Caucasian men. Conclusions: In REDUCE, where all men underwent biopsies largely independent of PSA, Asian race was associated with lower prostate cancer diagnosis. Impact: These findings suggest that lower prostate cancer risk in Asian men may be due to biological, genetic, and/or lifestyle factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".