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Record W1835649288 · doi:10.1111/hdi.12159

Hypomagnesemia, chronic kidney disease and cardiovascular mortality: Pronounced association but unproven causation

2014· review· en· W1835649288 on OpenAlexvenueno aff
Periklis Dousdampanis, Konstantina Trigka, Costas Fourtounas

Bibliographic record

VenueHemodialysis International · 2014
Typereview
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsnot available
Fundersnot available
KeywordsHypomagnesemiaMedicineKidney diseaseDialysisDiabetes mellitusInternal medicineDiseaseSystemic inflammationHemodialysisMagnesium deficiency (plants)Insulin resistanceKidneyInflammationNephrologyIntensive care medicineMagnesiumEndocrinology

Abstract

fetched live from OpenAlex

Magnesium is as an essential metal implicated in numerous physiological functions of human cells. The kidney plays a crucial role in magnesium homeostasis. In advanced chronic kidney disease, serum magnesium levels are increased. Data from experimental and observational studies suggest that low levels of magnesium are associated with several factors, such as insulin resistance, diabetes, oxidative stress, hypertension, atherosclerosis, and inflammation which are implicated in the progression of chronic kidney disease. Moreover, low levels of magnesium have been correlated with cardiovascular disease and all-cause mortality in end-stage renal disease patients. Hypomagnesemia has also been associated with poorer renal allograft and transplant recipients' outcomes. The causality of these relationships has not been completely elucidated. A thorough review of the current literature indicates that low magnesium levels in dialysis patients may reflect a poorer nutritional status and/or are the result of systemic inflammation. Further studies in chronic kidney disease and dialysis patients are needed in order to clarify the causality of these associations.

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)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
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 head0.027
GPT teacher head0.315
Teacher spread0.288 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations28
Published2014
Admission routes1
Has abstractyes

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