Serum selenium levels are inversely associated with death risk among hemodialysis patients
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that serum selenium levels are decreased in hemodialysis patients. Selenium deficiency may contribute to an increased risk for death among hemodialysis patients. METHODS: A population-based prospective cohort study in adult hemodialysis patients was conducted. A total of 1041 patients were enrolled. Patients were divided into quartile groups according to serum selenium levels. Mortality rates between the groups were compared by the log-rank test. Associations between serum selenium levels and cause-specific mortality risks in hemodialysis patients were examined by Cox's regression model. RESULTS: A total of 382 patients died during the 5-year follow-up period (median follow-up period, 4.9 years). Crude mortality rates in quartile groups according to serum selenium levels were 134.5, 99.9, 85.9 and 55.2 (per 1000 patient-years), respectively. The lowest quartile group had significantly higher mortality rates from all-cause and infectious disease-related death than the rates in the other three groups (P < 0.001, by log-rank test). Mortality rates from cardiovascular and malignant disease-related death were similar between the groups. A strong inverse relationship between selenium levels and infectious disease-related death was observed even after multivariate adjustment (trend P = 0.024). CONCLUSIONS: Serum selenium levels were inversely associated with death risk, especially death risk due to infectious disease, among hemodialysis patients. Decreased serum selenium level may contribute to immunity dysfunction and may increase the risk of death from infectious disease in hemodialysis patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".