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Record W3134699247 · doi:10.1111/hdi.12916

Vitamin D and skeletal muscle: A narrative review focusing on chronic kidney disease and dialysis

2021· review· en· W3134699247 on OpenAlexvenueno aff
Bárbara Perez Vogt, Jacqueline Costa Teixeira Caramori

Bibliographic record

VenueHemodialysis International · 2021
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKlothoSkeletal muscleCalcitriolKidney diseaseVitamin D and neurologyCalcitriol receptorInternal medicineEndocrinologyDialysisHemodialysisKidney

Abstract

fetched live from OpenAlex

Morphological, molecular, and physiological effects of vitamin D on skeletal muscle have been analyzed both in animals and humans. Vitamin D may be a potential therapeutic for increasing muscle mass and function. The presence of vitamin D receptors in skeletal muscle cells is already established. However, there is still need for more evidence about the effect of vitamin D on muscle. Some studies have associated vitamin D and skeletal muscle in chronic kidney disease (CKD) patients; most of these studies enrolled hemodialysis patients. FGF-23 and Klotho were recently described in mineral and bone disorders in CKD, resulting in reductions in calcitriol levels. Therefore, both Klotho and FGF-23 may play a role in muscle loss in CKD, which is related to morbidity and mortality risk. Therefore, this article presents a narrative review, aiming to discuss the available information associating skeletal muscle and vitamin D, highlighting the results in CKD and dialysis patients.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.006
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.035
GPT teacher head0.371
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

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