Abstract 16: Effect of Long-Term Selenium Supplementation on Mortality: Results From a Multiple-Dose Randomised Controlled Trial
Bibliographic record
Abstract
Background: Selenium (Se) is an essential trace element that is incorporated into selenoproteins with a wide range of health effects. The concentration of selenoproteins plateaus at plasma Se concentrations ~125 μg/L, however, and it is unclear whether Se supplementation is beneficial above this plateau. Methods: In a double-blind, randomized, controlled trial of Se supplementation and mortality in Denmark, a population of moderately-low Se status, 491 men and women, 60-74 years of age, were treated with 100, 200, or 300 μg Se/d as Se-enriched yeast or placebo-yeast. From 2008-2009, active treatment was provided for 5 years and post-treatment follow-up for 10 additional years. During 6,871 person-years of follow-up, there were 158 deaths (31 during active treatment and 127 after treatment cessation). Results: The hazard ratio (95% confidence interval [CI]) for all-cause mortality comparing 300 μg Se/d to placebo was 1.62 (0.66 to 3.96) after 5 years of treatment and 1.59 (1.02 to 2.46) over the entire follow-up. Se doses of 100 and 200 μg/d non-significantly decreased mortality during the intervention period but their effects vanished after treatment cessation. The effects on cancer and cardiovascular mortality were similar but less precise than on all-cause mortality. Conclusions: A dose of 300 μg/d of Se (as high-Se yeast) taken for five years in a country with moderately-low Se status increased all-cause mortality 10 years later. Lower doses showed a non-significant reduction in mortality which dissipated after treatment discontinuation. Total Se intake (diet plus supplements) over 300 μg/d should be avoided.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".