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Record W1918328440 · doi:10.1111/hdi.12091

Epicardial fat thickness is associated with impaired coronary flow reserve in hemodialysis patients

2013· article· en· W1918328440 on OpenAlexvenueno aff
Aydın Atakan, Beyza Macunluoğlu, Yüksel Kaya, Elif Arı, Halit Demir, Ebru Aşıcıoğlu, Çiğdem Kaspar

Bibliographic record

VenueHemodialysis International · 2013
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronary flow reserveCardiologyInternal medicineHemodialysisEpicardial fatCoronary artery diseaseBody mass indexPopulationArteryAdipose tissue

Abstract

fetched live from OpenAlex

Cardiovascular disease (CVD) is the main cause of mortality in hemodialysis (HD) patients. Epicardial fat tissue (EFT) is a new risk factor in CVD. The aim of this study was to evaluate the association between EFT and coronary artery flow reserve (CFR), which is an early indicator of endothelial dysfunction in coronary vessels of HD patients. We performed a cross-sectional study including 71 chronic HD patients and 65 age- and sex-matched healthy controls. Epicardial fat tissue was significantly higher in HD patients when compared to healthy controls (6.53 ± 1.01 mm vs. 5.79 ± 1.06 mm, respectively, P < 0.001). On transthoracic Doppler echocardiography, CFR values were significantly lower in HD patients when compared to healthy controls (1.73 ± 0.11 vs. 2.32 ± 0.28, P < 0.001). Correlation analysis showed CFR values to be inversely correlated with EFT (r = -0.287, P < 0.05). Multiple linear regression analysis was used to define independent determinants of EFT in HD patients. Artery flow reserve, age, body mass index and total cholesterol levels were independently correlated with EFT thickness. This study demonstrated that EFT was significantly higher among HD patients compared to healthy controls. In addition, this study was the first to demonstrate an inverse correlation between EFT and CFR in this patient population.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.232
Teacher spread0.221 · 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
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

Citations19
Published2013
Admission routes1
Has abstractyes

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