Risk of prostate cancer across different racial/ethnic groups in men with diabetes: a retrospective cohort study
Bibliographic record
Abstract
AIM: To examine the associations between prostate cancer, diabetes and race/ethnicity. METHODS: Using administrative data from British Columbia, Canada for the period 1994 to 2012, we identified men aged ≥50 years with and without diabetes. Validated surname algorithms identified men as Chinese, Indian or of other race/ethnicity. Multivariable Cox regression was used to estimate adjusted risks of prostate cancer according to diabetes status and race/ethnicity. RESULTS: Our cohort of 160 566 men had a mean (sd) age of 64.7 (9.4) years and a median of 9 years' follow-up. The incidence rates of prostate cancer among those with and without diabetes were 177.4 (171.7-183.4) and 216.0 (209.7-222.5) per 1000 person-years, respectively. The incidence among Chinese men was 120.9 (109.2-133.1), among Indian men it was 144.1 (122.8-169.0) and in men of other ethnicity it was 204.8 (200.2-209.5). Diabetes was independently associated with a lower risk of prostate cancer (adjusted hazard ratio 0.82, 95% CI 0.78-0.86), as was Chinese (adjusted hazard ratio 0.54, 95% CI 0.46,0.63) and Indian (adjusted hazard ratio 0.66, 95% CI 0.49,0.89) race/ethnicity; however, there was no statistically significant interaction between diabetes status and race/ethnicity (all P>0.1). CONCLUSION: Diabetes and Chinese and Indian race/ethnicity were each independently associated with a lower risk of prostate cancer.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".