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Record W4362468562 · doi:10.3390/nu15071710

Magnesium in Kidney Function and Disease—Implications for Aging and Sex—A Narrative Review

2023· review· en· W4362468562 on OpenAlexafffund
María del Carmen Macías Ruiz, Lorena Cuenca‐Bermejo, Nicola Veronese, Emiliano Fernández Villalba, Ana María González Cuello, Karolina Kublickiene, Valeria Raparelli, Colleen M. Norris, Alexandra Kautzky‐Willer, Louise Pilote, Mario Barbagallo, Ligia J. Domínguez, María Trinidad Herrero

Bibliographic record

VenueNutrients · 2023
Typereview
Languageen
FieldNursing
TopicMagnesium in Health and Disease
Canadian institutionsMcGill University Health CentreAlberta HealthUniversity of AlbertaAlberta Health Services
FundersUniversidad de MurciaVetenskapsrådetCanadian Institutes of Health ResearchAustrian Science Fund
KeywordsKidney diseaseMedicineRenal functionNarrative reviewClinical trialDiseasePopulationInternal medicineIntensive care medicineGerontologyPhysiologyEndocrinologyEnvironmental health

Abstract

fetched live from OpenAlex

Magnesium (Mg) has a vital role in the human body, and the kidney is a key organ in the metabolism and excretion of this cation. The objective of this work is to compile the available evidence regarding the role that Mg plays in health and disease, with a special focus on the elderly population with chronic kidney disease (CKD) and the eventual sex differences. A narrative review was carried out by executing an exhaustive search in the PubMed, Scopus, and Cochrane databases. Ten studies were found in which the role of Mg and sex was evaluated in elderly patients with CKD in the last 10 years (2012-2022). The progression of CKD leads to alterations in mineral metabolism, which worsen as the disease progresses. Mg can be used as a coadjuvant in the treatment of CKD patients to improve glomerular filtration, but its use in clinical applications needs to be further characterized. In conclusion, there's a need for well-designed prospective clinical trials to advise and standardize Mg supplementation in daily clinical practice, taking age and sex into consideration.

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)0.001
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 score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.398
Teacher spread0.324 · 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

Citations20
Published2023
Admission routes2
Has abstractyes

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