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Mechanisms of Vascular Calcification and Associated Diseases

2014· review· en· W2157968836 on OpenAlexaff
Juliana Marulanda, Saleh Alqarni, Monzur Murshed

Bibliographic record

VenueCurrent Pharmaceutical Design · 2014
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsCalcificationMedicineCalcinosisIntensive care medicinePathology

Abstract

fetched live from OpenAlex

Mineralization of bone and tooth extracellular matrix (ECM) is a physiologic process, while soft tissue mineralization, also known as ectopic mineralization (calcification), is a pathologic condition. Vascular calcification is common in aging and also in a number of genetic and metabolic disorders. The calcific deposits in arteries complicate the prognosis and increase the morbidity in diseases such as atherosclerosis, diabetes and chronic kidney disease (CKD). To completely understand the pathophysiology of these lifethreatening diseases, it is critical to elucidate the molecular mechanisms underlying vascular calcification. Unveiling these mechanisms will eventually identify new therapeutic targets and also improve the management of the associated complications. In the current review, we discussed the common determinants of ECM mineralization, the mechanism of vascular calcification associated with several human diseases and outlined the most common therapeutic approaches to prevent its progression.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.188
GPT teacher head0.453
Teacher spread0.265 · 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 designOther design
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

Citations25
Published2014
Admission routes1
Has abstractyes

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