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Record W114184580

Abdominal obesity and cardiovascular disease risk factors within body mass index categories.

2012· article· en· W114184580 on OpenAlexaffabout
Margot Shields, Mark S. Tremblay, Sarah Connor Gorber, Ian Janssen

Bibliographic record

VenuePubMed · 2012
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsAbdominal obesityMedicineBody mass indexWaist-to-height ratioObesityWaistOverweightOdds ratioWaist–hip ratioDemographyLogistic regressionMetabolic syndromeInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Several organizations recommend the use of measures of abdominal obesity in conjunction with body mass index (BMI) to assess obesity-related health risk. Recent evidence suggests that waist circumference (WC), waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) are increasing within BMI categories. This shift may have affected the usefulness of abdominal obesity measures. DATA AND METHODS: Data are from respondents aged 18 to 79 to the 2007 to 2009 Canadian Health Measures Survey. Using logistic regression, this paper examines cardiovascular disease (CVD) risk factors in relation to WC, WHR and WHtR within BMI health-risk categories. CVD risk factors considered include components of the metabolic syndrome. RESULTS: Among men in the normal and overweight BMI categories, WHR and WHtR were positively associated with having at least two CVD risk factors. All three abdominal obesity measures were associated with increased odds of having at least two CVD risk factors among normal-weight women. Abdominal obesity was not associated with CVD risk factors for people in obese class I. INTERPRETATION: Among men and women in the normal BMI category, measures of abdominal obesity are associated with increased odds of CVD risk factors. This underscores the importance of measuring and monitoring abdominal obesity in normal-weight men and women.

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)0.002
metaresearch head score (Gemma)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.014
GPT teacher head0.198
Teacher spread0.183 · 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

Citations80
Published2012
Admission routes2
Has abstractyes

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