Trace elements in hemodialysis patients: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Hemodialysis patients are at risk for deficiency of essential trace elements and excess of toxic trace elements, both of which can affect health. We conducted a systematic review to summarize existing literature on trace element status in hemodialysis patients. METHODS: All studies which reported relevant data for chronic hemodialysis patients and a healthy control population were eligible, regardless of language or publication status. We included studies which measured at least one of the following elements in whole blood, serum, or plasma: antimony, arsenic, boron, cadmium, chromium, cobalt, copper, fluorine, iodine, lead, manganese, mercury, molybdenum, nickel, selenium, tellurium, thallium, vanadium, and zinc. We calculated differences between hemodialysis patients and controls using the differences in mean trace element level, divided by the pooled standard deviation. RESULTS: We identified 128 eligible studies. Available data suggested that levels of cadmium, chromium, copper, lead, and vanadium were higher and that levels of selenium, zinc and manganese were lower in hemodialysis patients, compared with controls. Pooled standard mean differences exceeded 0.8 standard deviation units (a large difference) higher than controls for cadmium, chromium, vanadium, and lower than controls for selenium, zinc, and manganese. No studies reported data on antimony, iodine, tellurium, and thallium concentrations. CONCLUSION: Average blood levels of biologically important trace elements were substantially different in hemodialysis patients, compared with healthy controls. Since both deficiency and excess of trace elements are potentially harmful yet amenable to therapy, the hypothesis that trace element status influences the risk of adverse clinical outcomes is worthy of investigation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.020 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it