Relationship between Serum Magnesium, Parathyroid Hormone, and Vascular Calcification in Patients on Dialysis: A Literature Review
Bibliographic record
Abstract
Secondary hyperparathyroidism is present in most patients with end-stage renal disease and has been linked to uremic bone disease, vascular calcification, and mortality. Current literature suggests an association between hypomagnesemia and cardiovascular disease in the general population. We reviewed all published studies on the relationship between serum magnesium and parathyroid hormone and the relationship between serum Mg and vascular calcification in dialysis patients. Of these, 10 of 12 studies of patients on hemodialysis and 4 of 5 studies of patients on peritoneal dialysis showed a significantinverse relationship between serum Mg and serum intact parathyroid hormone. Hyperparathyroidism develops in peritoneal dialysis patients dialyzed with a solution containing normal calcium (1.25 mmol/L) and low Mg (0.25 mmol/L), even though serum calcium is maintained at a normal level. Four of the hemodialysis studies and one of the peritoneal dialysis studies indicated that there is an inverse relationship between serum Mg and vascular calcification in these patients. Potential benefits have been attributed to magnesium carbonate as a phosphate binder and it may possibly be an effective, less toxic, less expensive phosphate binder. We believe that the role of Mg in secondary hyperparathyroidism and vascular calcification merits further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".