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Record W2408968722 · doi:10.1097/mnh.0000000000000161

Epicardial adipose tissue in patients with end-stage renal disease on haemodialysis

2015· review· en· W2408968722 on OpenAlexaff
Matthew Graham‐Brown, Gerry P McCann, James O. Burton

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2015
Typereview
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsInstitute of Infection and Immunity
FundersNational Institute for Health and Care Research
KeywordsMedicinePathogenesisCoronary artery diseaseAdipose tissueDiseaseInternal medicineCardiologyPathophysiologyBioinformatics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Epicardial adipose tissue (EAT) is the visceral fat of the heart, sharing many of the pathophysiological properties of other visceral fat depots. EAT is a metabolically active paracrine and vasocrine organ that causes local cardiac inflammation and is strongly implicated in the pathogenesis of coronary atherosclerosis. This article highlights the findings of recent observational studies in patients on haemodialysis that link the quantity of EAT to increased rates of cardiovascular and coronary artery disease and review the proposed methods of pathogenesis and the possible role of EAT quantification to improve cardiovascular risk assessment. RECENT FINDINGS: Increasing volumes of EAT in patients on haemodialysis correlate with increased inflammatory mediators, higher rates of cardiovascular disease and coronary artery calcification, independent of general adiposity. EAT is an independent predictor of mortality and a potentially modifiable target for therapeutic interventions. SUMMARY: EAT is likely to play a central role in the pathogenesis of cardiovascular disease in patients on haemodialysis, adds incrementally to conventional cardiovascular risk stratification models and is a potential target for therapeutic intervention.

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 score0.977
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.067
GPT teacher head0.339
Teacher spread0.272 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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