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Record W2912503275 · doi:10.1097/med.0000000000000471

Risk factors for valvular calcification

2019· review· en· W2912503275 on OpenAlexafffund
Hao Yu Chen, James C. Engert, George Thanassoulis

Bibliographic record

VenueCurrent Opinion in Endocrinology Diabetes and Obesity · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsMedicineCalcificationInternal medicineCardiologyStenosisDiseaseCalcinosisRisk factorAortic valve

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Recent literature is examined to identify established and emerging risk factors for valvular calcification, specifically calcific aortic valve disease and mitral annular calcification. RECENT FINDINGS: Strong evidence implicates older age, male sex, cigarette smoking, elevated blood pressure, dyslipidaemia, adiposity, and mineral metabolism as risk factors for calcific aortic valve disease. Emerging evidence suggests family history and lipoprotein(a) are additional risk factors. Recently, large-scale genome-wide analyses have identified robust associations for LPA, PALMD, and TEX41 with aortic stenosis. Factors predisposing to mitral annular calcification are less well characterized. Older age, cigarette smoking, increased BMI, kidney dysfunction, and elevated triglycerides are associated with greater risk of mitral annular calcification, but conflicting evidence exists for sex and C-reactive protein. SUMMARY: Established and emerging risk factors for calcific aortic valve disease, including some that overlap with atherosclerosis, may represent targets for pharmacological intervention. Mitral annular calcification is comparatively less well understood though some atherosclerosis risk factors do appear to increase risk.

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: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
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 head0.096
GPT teacher head0.432
Teacher spread0.337 · 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 designObservational
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

Citations63
Published2019
Admission routes2
Has abstractyes

Explore more

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