Lower serum magnesium is associated with vascular calcification in peritoneal dialysis patients: a cross sectional study
Bibliographic record
Abstract
Coronary artery calcification (CAC) is highly prevalent among dialysis patients and is associated with increased cardiovascular and all cause mortality. Magnesium (Mg) inhibits vascular calcification in animal and in-vitro studies but whether the same effect occurs in humans is uncertain. A single centre cross-sectional study of 80 prevalent peritoneal dialysis (PD) patients; on PD only for a minimum of 3 months. A radiologist blinded to patient status calculated their abdominal aortic calcification (AAC) scores on lateral lumbar spine radiographs, a validated surrogate for CAC. Eighty patients provided informed consent and underwent lumbar spine radiography. The mean serum Mg was 0.8 mmol/L (standard deviation 0.2) and mean AAC score 8.9 (minimum 0, maximum 24). A higher serum Mg level was associated with a lower AAC score (R 2 = 0.06, unstandardized coefficient [B] = −7.81, p = 0.03), and remained after adjustment for age, serum phosphate, serum parathyroid hormone, low-density lipoprotein cholesterol, smoking history, and diabetes (model adjusted R 2 = 0.36, serum Mg and AAC score B = −11.44, p = 0.00). This translates to a 0.1 mmol/L increase in serum Mg being independently associated with a 1.1-point decrease in AAC score. Our findings suggest that Mg may inhibit vascular calcification. If this association is replicated across larger studies with serial Mg and vascular calcification measurements, interventions that increase serum Mg and their effect on vascular calcification warrant further investigation in the PD population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".