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Record W2123486302 · doi:10.1093/eurheartj/ehp371

Does abdominal obesity have a similar impact on cardiovascular disease and diabetes? A study of 91 246 ambulant patients in 27 European Countries

2009· article· en· W2123486302 on OpenAlexaff
Keith A.A. Fox, Jean‐Philippe Després, A Richard, Stéphanie Brette, John Deanfield

Bibliographic record

VenueEuropean Heart Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicDiabetes, Cardiovascular Risks, and Lipoproteins
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMedicineAbdominal obesityObesityWaistDiabetes mellitusBody mass indexOdds ratioRisk factorInternal medicineDiseaseDemographyEndocrinology

Abstract

fetched live from OpenAlex

AIMS: Differences in cardiovascular risk factors across Europe provide an opportunity to examine the impact of adiposity on the frequency of diabetes and cardiovascular disease (CVD). METHODS AND RESULTS: The International Day for Evaluation of Abdominal obesity (IDEA) study evaluated the prevalence of abdominal obesity, elevated body mass index (BMI), and other cardiometabolic risk factors among primary care patients. Abdominal obesity predicted increased diabetes risk, despite socio-economic, demographic, and risk factor differences. Cardiovascular disease was at least two-fold more frequent in Eastern Europe vs. Northwest Europe (P < 0.0001) and 2.5-fold more vs. Southern Europe (P < 0.0001). Waist circumference (WC) predicted increased (P < 0.0001) age- and BMI-adjusted risks of CVD and diabetes. In women, odds ratios (95% confidence intervals) for CVD per 1 SD increase in WC were: Northwest Europe 1.28 (1.18-1.40); Southern Europe 1.26 (1.16-1.37); and Eastern Europe 1.10 (1.03-1.18). Values for diabetes were 1.72 (1.58-1.88), 1.45 (1.35-1.56), and 1.59 (1.46-1.73), with similar findings in men. CONCLUSION: Abdominal obesity impacted similarly on the frequency of diabetes across Europe, despite regional differences in cardiovascular risk factors and CVD rates. Increasing abdominal obesity may offset future declines in CVD, even where CVD rates are lower.

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)0.003
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 score0.026
Threshold uncertainty score0.965

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 integrity0.0000.001
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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations59
Published2009
Admission routes1
Has abstractyes

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