Trace element supplementation in hemodialysis patients: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: People with kidney failure are often deficient in zinc and selenium, but little is known about the optimal way to correct such deficiency. METHODS: We did a double-blind randomized trial evaluating the effects of zinc (Zn), selenium (Se) and vitamin E added to the standard oral renal vitamin supplement (B and C vitamins) among hemodialysis patients in Alberta, Canada. We evaluated the effect of two daily doses of the new supplement (medium dose: 50 mg Zn, 75 mcg Se, 250 IU vitamin E; low dose: 25 mg Zn, 50 mcg Se, 250 IU vitamin E) compared to the standard supplement on blood concentrations of Se and Zn at 90 days (primary outcome) and 180 days (secondary outcome) as well as safety outcomes. RESULTS: We enrolled 150 participants. The proportion of participants with low zinc status (blood level <815 ug/L) did not differ between the control group and the two intervention groups at 90 days (control 23.9% vs combined intervention groups 23.9%, P > 0.99) or 180 days (18.6% vs 28.2%, P = 0.24). The proportion with low selenium status (blood level <121 ug/L) was similar for controls and the combined intervention groups at 90 days (32.6 vs 19.6%, P = 0.09) and 180 days (34.9% vs 23.5%, P = 0.17). There were no significant differences in the risk of adverse events between the groups. CONCLUSIONS: Supplementation with low or medium doses of zinc and selenium did not correct low zinc or selenium status in hemodialysis patients. Future studies should consider higher doses of zinc (≥75 mg/d) and selenium (≥100 mcg/d) with the standard supplement. TRIAL REGISTRATION: Registered with ClinicalTrials.gov (NCT01473914).
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".