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Record W2074880745 · doi:10.1016/s0019-4832(12)60088-1

Visceral adiposity in young patients with coronary artery disease—a case control study

2012· article· en· W2074880745 on OpenAlexfundno aff
Blessan Varghese, Smrita Swamy, Mounika Srilakshmi, M.J. Santhosh, Gurappa G. Shetty, Kiron Varghese, Chandrakant Patil, Shamanna S. Iyengar

Bibliographic record

VenueIndian Heart Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Disease and Adiposity
Canadian institutionsnot available
FundersNational Institute of Animal Nutrition and PhysiologyMcMaster University
KeywordsMedicineCoronary artery diseaseCardiologyInternal medicineBody mass indexVisceral fatWaistCase-control studyUmbilicus (mollusc)Intra-Abdominal FatArteryObesityGastroenterologySurgeryInsulin resistance

Abstract

fetched live from OpenAlex

AIMS: Central obesity is associated with an increased cardiovascular risk. We carried out a hospital based case control study in young patients with coronary artery disease (CAD) to assess the importance of visceral fat METHODS: Coronary artery disease was established by coronary angiogram in all cases. Controls were age- and sex-matched subjects with normal coronary angiogram. Computed tomography scan performed at the level of the umbilicus to measure subcutaneous and visceral fat area (VFA). RESULTS: Cases and controls were well matched in height, weight, and body mass index (BMI). Visceral fat area was significantly higher (122.58 ± 37.59 vs. 88.4 ± 36.95 cm(2); P=0.003) in cases whereas subcutaneous fat area was similar in cases and controls. Visceral fat area was an excellent predictor of cardiovascular risk (area under receiver operating characteristics curve 0.915 cm(2)). Visceral fat area correlated with BMI, waist hip ratio, blood sugar, triglycerides, and C-reactive protein significantly. CONCLUSION: Visceral adiposity is associated with an increased risk of CAD and it correlated with anthropometric, metabolic, and inflammatory markers.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.246
Teacher spread0.237 · 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.

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

Citations9
Published2012
Admission routes1
Has abstractyes

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