Trace Element Status in Hemodialysis Patients
Bibliographic record
Abstract
Patients with chronic kidney disease undergoing hemodialysis (HD) are potentially at risk of deficiency and excess of trace elements. HD exposes patients to large volumes of water (>120 l/week) in the form of dialysate. Although levels of certain ions (such as potassium and calcium) are carefully regulated in dialysate, many others are measured infrequently, if ever. As a result, substances in lower concentrations in the dialysis may be leached from the body. Conversely, toxic trace elements present in water but not in blood may accumulate and cause toxicity. Given that essential trace elements play key roles in multiple biological systems including immunological defense against oxidation and infection, it has been hypothesized that the increased morbidity and mortality seen in HD patients may in part be due to the imbalance of trace elements that has not been recognized. A recent systematic review has shown that compared with healthy controls, HD patients have significantly lower blood levels of zinc, manganese, and selenium, while blood levels of lead are likely to accumulate. Other trace elements, such as mercury and arsenic, are biologically plausible causes of excess mortality in dialysis patients, but available evidence is inconclusive as to whether they consistently accumulate in this population. Whether altered trace element levels are potentially reversible causes of adverse clinical outcomes in dialysis patients remains to be determined. This review highlights key issues related to this hypothesis, with special emphasis on zinc, manganese, selenium, lead, mercury, and arsenic.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".