Prevalence of Prostate Cancer on Autopsy: Cross-Sectional Study on Unscreened Caucasian and Asian Men
Bibliographic record
Abstract
BACKGROUND: Substantial geographical differences in prostate cancer (PCa) incidence and mortality exist, being lower among Asian (ASI) men compared with Caucasian (CAU) men. We prospectively compared PCa prevalence in CAU and ASI men from specific populations with low penetrance of prostate-specific antigen screening. METHODS: Prostate glands were prospectively obtained during autopsy from men who died from causes other than PCa in Moscow, Russia (CAU), and Tokyo, Japan (ASI). Prostates were removed en-block and analyzed in toto. We compared across the 2 populations PCa prevalence, number and Gleason score (GS) of tumour foci, pathological stage, spatial location, and tumor volume using χ(2), Mann-Whitney-Wilcoxon tests, and multiple logistic regression. All statistical tests were two-sided. RESULTS: Three hundred twenty prostates were collected, 220 from CAU men and 100 from ASI mean. The mean age was 62.5 in CAU men and 68.5 years in ASI men (P < .001). PCa prevalences of 37.3% in CAU men and 35.0% in ASI men were observed (P = .70). Average tumor volume was 0.303cm(3). In men aged greater than 60 years, PCa was observed in more than 40% of prostates, reaching nearly 60% in men aged greater than 80 years. GS 7 or greater cancers accounted for 23.1% and 51.4% of all PCa in CAU and ASI men, respectively, (P = .003). When adjusted for age and prostate weight, ASI men still had a greater probability of having GS 7 or greater PCa (P = .03). CONCLUSIONS: PCa is found on autopsy in a similar proportion of Russian and Japanese men. More than 50% of cancers in ASI and nearly 25% of cancers in CAU men have a GS of 7 or greater. Our results suggest that the definition of clinically insignificant PCa might be worth re-examining.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".