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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".