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Long‐term effects of magnesium carbonate on coronary artery calcification and bone mineral density in hemodialysis patients: A pilot study

2009· article· en· W2037638796 on OpenAlexvenueno aff
David M. Spiegel, Beverly Farmer

Bibliographic record

VenueHemodialysis International · 2009
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePhosphate binderBone mineralMagnesiumHemodialysisCalcificationInternal medicineDialysisCalciumOsteoporosisSurgeryMetallurgyHyperphosphatemiaMaterials science

Abstract

fetched live from OpenAlex

Observational data suggest that elevated magnesium levels in dialysis patients may prevent vascular calcification and in vitro magnesium can prevent hydroxyapatite crystal growth. However, the effects of magnesium on vascular calcification and bone mineral density have not been studied prospectively. Seven chronic hemodialysis patients participated in this open label, prospective pilot study to evaluate the effects of a magnesium-based phosphate binder on coronary artery calcification (CAC) scores and vertebral bone mineral density (V-BMD) in patients with baseline CAC scores >30. Magnesium carbonate/calcium carbonate (elemental Mg: 86 mg/elemental Ca 100 mg) was administered as the principal phosphate binder for a period of 18 months and changes in CAC and V-BMD were measured at baseline, 6, 12, and 18 months. Serum magnesium levels averaged 2.2+/-0.4 mEq/L (range: 1.3-3.9 mEq/L). Phosphorus levels (4.5+/-0.6 mg/dL) were well controlled throughout the 18 months study. Electron beam computed tomography results demonstrated a small not statically significant increase in absolute CAC scores, no significant change in median percent change, and a small none significant change in V-BMD. Magnesium may have a favorable effect on CAC. The long-term effect on bone mineral density remains unclear. Larger studies are needed to confirm these findings.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.265
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations93
Published2009
Admission routes1
Has abstractyes

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