Plasma Tocopherols and Risk of Prostate Cancer in the Selenium and Vitamin E Cancer Prevention Trial (SELECT)
Bibliographic record
Abstract
The Selenium and Vitamin E Cancer Prevention Trial (SELECT) showed higher prostate cancer incidence in men supplemented with high-dose α-tocopherol. We, therefore, examined whether presupplementation plasma α-tocopherol or γ-tocopherol was associated with overall or high-grade prostate cancer. A stratified case-cohort sample that included 1,746 incident prostate cancer cases diagnosed through June 2009 and a subcohort of 3,211 men was derived from the SELECT trial of 35,533 men. Plasma was collected at entry from 2001 to 2004, and median follow-up was 5.5 years (range, 0-7.9 years). Incidence of prostate cancer as a function of plasma α-tocopherol, γ-tocopherol, and supplementation with α-tocopherol or selenomethionine was estimated by the hazard ratio (HR). Plasma γ-tocopherol was not associated with prostate cancer. Men with higher α-tocopherol concentrations seemed to have risk similar to that of men with lower concentrations [overall HR for fifth (Q5) vs. first quintile (Q1), 1.21; 95 % confidence interval (CI), 0.88-1.66; P-trend = 0.24; in the trial placebo arm, Q5 HR, 0.85; 95% CI, 0.44-1.62; P-trend = 0.66]. We found a strong positive plasma α-tocopherol association among men receiving the trial selenomethionine supplement [Q5 HR, 2.04; 95% CI, 1.29-3.22; P-trend = 0.005]. A positive plasma α-tocopherol-prostate cancer association also seemed limited to high-grade disease (Gleason grade, 7-10; overall Q5 HR, 1.59; 95% CI, 1.13-2.24; P-trend = 0.001; among men receiving selenomethionine, Q5 HR, 2.12; 95% CI, 1.32-3.40; P-trend = 0.0002). Our findings indicate that higher plasma α-tocopherol concentrations may interact with selenomethionine supplements to increase high-grade prostate cancer risk, suggesting a biologic interaction between α-tocopherol and selenium itself or selenomethionine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".