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Record W4366782729 · doi:10.31083/j.rcm2404111

Understanding the Role of Vitamin D in Heart Failure

2023· review· en· W4366782729 on OpenAlexaff
Paramjit S. Tappia, Rhea Carina T. Lopez, Shirley Fitzpatrick‐Wong, Bram Ramjiawan

Bibliographic record

VenueReviews in Cardiovascular Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
Fundersnot available
KeywordsMedicineHeart failureVitamin D and neurologyCardioprotectionCardiac function curvePathogenesisInternal medicineCardiologyVitaminClinical significanceBioinformaticsMyocardial infarctionBiology

Abstract

fetched live from OpenAlex

Vitamin D is now believed to have a significant role in cardiac signal transduction and regulation of gene expression, and thus influences normal cardiomyocyte function. It has been reported to provide cardioprotection through its anti-inflammatory, anti-apoptotic and anti-fibrotic actions; and to prevent cardiac remodeling, Ca2+-handling defects, and abnormal electrophysiological patterns. A vitamin D deficient state has been associated in the pathogenesis of heart failure; however, while many clinical studies report a benefit of vitamin D to heart function, other clinical studies are inconsistent with these findings. These uncertainties have led to a discord in the recommendation of vitamin D supplementation for the treatment of heart failure or as a preventive agent in patients deemed to be at risk for cardiac dysfunction. Accordingly, this article is intended to describe some of the mechanisms/sites of action of vitamin D in different animal models of heart failure, as well as to review the clinical observations and challenges in the interpretation and understanding of the clinical relevance of vitamin D in relation to heart function.

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)0.001
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.190
GPT teacher head0.393
Teacher spread0.203 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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