Effect of magnesium oxide or citrate supplements on metabolic risk factors in kidney stone formers with idiopathic hyperoxaluria: a randomized clinical trial
Bibliographic record
Abstract
Magnesium is one of the recommended treatments for calcium stone formers (CSFs) with hyperoxaluria. In this study, we compared the effect of magnesium oxide (MgO) or magnesium citrate (MgCit) with placebo on 24-hour urine (24-U) metabolites and the calcium oxalate supersaturation index (CaOx SS). In a randomized, double-blind, placebo-controlled clinical trial, 90 CSFs with idiopathic hyperoxaluria were recruited from a tertiary stone prevention clinic. Patients were randomly assigned into three groups: 120 mg MgO, 120 mg MgCit or placebo (supplements were taken three times per day, with meals). Finally, 76 patients were included in the final analysis. Analyses of 24-U were performed at baseline and after eight weeks. Study outcomes included changes in 24-U oxalate, magnesium, citrate, and CaOx SS. Dietary factors were controlled by 24-hour food recalls. Repeated measure ANOVA was used to compare the results. After the intervention, both MgO and MgCit supplements decreased 24-U oxalate excretion (-8.13±16.45 in the MgO group and -16.99±18.02 in the MgCit group) and CaOx SS compared to the placebo, with the effects of MgCit reaching statistical significance (p=0.011 and p=0.010, respectively). An increasing trend was observed for 24-U magnesium and citrate excretion without significant differences among groups. Interestingly, MgCit exhibited a significantly greater inhibitory effect on 24-U oxalate in patients with normal urine magnesium levels (p=0.021). Clinically, both MgO and MgCit reduced 24-U oxalate and CaOx SS compared to placebo. However, MgCit demonstrated a greater effect, especially in patients with normal urine magnesium levels.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".