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Record W4393352563 · doi:10.3390/biomedicines12040768

Vitamin D and Cardiovascular Diseases: From Physiology to Pathophysiology and Outcomes

2024· article· en· W4393352563 on OpenAlexaff
Matteo Nardin, Monica Verdoia, Simone Nardin, Davide Cao, Mauro Chiarito, Elvin Kedhi, Gennaro Galasso, Gianluigi Condorelli, Giuseppe De Luca

Bibliographic record

VenueBiomedicines · 2024
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsVitamin D and neurologyMedicineCoronary artery diseasePathophysiologyDiseaseObservational studyHeart failureClinical trialvitamin D deficiencyInternal medicinePhysiologyEndothelial dysfunctionVitaminBioinformaticsEndocrinologyIntensive care medicineBiology

Abstract

fetched live from OpenAlex

Vitamin D is rightly recognized as an essential key factor in the regulation of calcium and phosphate homeostasis, affecting primary adequate bone mineralization. In the last decades, a more complex and wider role of vitamin D has been postulated and demonstrated. Cardiovascular diseases have been found to be strongly related to vitamin D levels, especially to its deficiency. Pre-clinical studies have suggested a direct role of vitamin D in the regulation of several pathophysiological pathways, such as endothelial dysfunction and platelet aggregation; moreover, observational data have confirmed the relationship with different conditions, including coronary artery disease, heart failure, and hypertension. Despite the significant evidence available so far, most clinical trials have failed to prove any positive impact of vitamin D supplements on cardiovascular outcomes. This discrepancy indicates the need for further information and knowledge about vitamin D metabolism and its effect on the cardiovascular system, in order to identify those patients who would benefit from vitamin D supplementation.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.296
Teacher spread0.285 · 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 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

Citations32
Published2024
Admission routes1
Has abstractyes

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