Serum Nickel Concentrations in Patients Receiving Chronic Hemodialysis
Bibliographic record
Abstract
INTRODUCTION: Chronic nickel accumulation is harmful to multiple organ systems, and nickel is classified as a human carcinogen. Nevertheless, few studies have examined serum nickel concentrations in end-stage kidney disease (ESKD) patients receiving chronic hemodialysis, and the relationship of serum nickel with clinical outcomes remains unclear. METHODS: This prospective observational study recruited 409 hemodialysis patients in 2019 and followed them for 18 months. The patients were stratified into four quartiles, that is, < 2.9 μg/L (n = 92), 2.9 μg/L to < 3.4 g/L (n = 104), 3.4 μg/L to < 3.9 μg/L (n = 104), and ≥ 3.9 μg/L (n = 109), according to their serum nickel concentrations. Baseline demographic, hematologic, biochemical, dialysis-related, and mortality data were obtained for analysis. FINDINGS: The mean age of the patients was 62.9 ± 11.7 years. A total of 401 (98.04%) patients had elevated serum nickel concentrations, with an average level of 3.6 ± 1.3 μg/L. Higher quartiles of serum nickel were associated with longer dialysis vintage (p < 0.001), higher Kt/V values (p < 0.001), and higher urea removal rates (p < 0.001). Multivariate analysis identified albumin level and dialysis vintage as independent factors positively correlated with serum nickel concentrations (R = 0.163, p = 0.001; R = 0.212, p < 0.001, respectively). Nevertheless, no association was found between serum nickel levels and all-cause mortality. CONCLUSION: ESKD patients on hemodialysis commonly exhibit elevated serum nickel concentrations, possibly linked to serum albumin levels and dialysis vintage. Further studies are warranted.
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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 | 0.001 |
| 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".