Concentrations of Trace Elements and Clinical Outcomes in Hemodialysis Patients
Bibliographic record
Abstract
Background and objectives Deficiency of essential trace elements and excess of potentially toxic trace elements are common in patients on hemodialysis. Whether these abnormalities are associated with poor outcomes is unknown but worth investigating, because they are potentially treatable. Design, setting, participants, & measurements We did a prospective longitudinal study of 1278 patients on incident hemodialysis, assessing blood concentrations of 25 trace elements at baseline. We used adjusted logistic regression to evaluate the association between trace element status and four outcomes (death, cardiovascular events, systemic infection, and hospitalization). A priori hypotheses concerned ( 1 ) deficiency of zinc, selenium, and manganese and ( 2 ) excess of lead, arsenic, and mercury. Concentrations of the other 19 elements were tested in hypothesis-generating analyses. Results Over 2 years of follow-up, 260 (20%) patients died, 285 (24%) experienced a cardiovascular event, 117 (10%) were hospitalized for systemic infection, and 928 (77%) were hospitalized for any cause. Lower concentrations of zinc or manganese and higher concentrations of lead, arsenic, or mercury were not independently associated with higher risk of clinical outcomes. Lower concentrations of selenium were strongly and independently associated with death (odds ratio, 0.86 per decile; 99.2% confidence interval, 0.80 to 0.93) and all-cause hospitalization (odds ratio, 0.92 per decile; 99.2% confidence interval, 0.86 to 0.98). In exploratory analyses, higher copper concentrations were significantly associated with higher risk of death (odds ratio, 1.07 per decile; 99.2% confidence interval, 1.00 to 1.15), and cadmium levels in the highest decile were associated with higher risk of death (odds ratio, 1.89; 99.2% confidence interval, 1.06 to 3.38). Conclusions Lower levels of zinc or manganese and higher concentrations of lead, arsenic, or mercury were not associated with higher risk of clinical outcomes, but lower concentrations of selenium were strongly and independently associated with the risks of death and hospitalization.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".