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Record W2021521383 · doi:10.5402/2012/816846

Metabolic Syndrome in Canadian Adults and Adolescents: Prevalence and Associated Dietary Intake

2012· article· en· W2021521383 on OpenAlexafffundabout
Solmaz Setayeshgar, Susan J. Whiting, Hassanali Vatanparast

Bibliographic record

VenueISRN Obesity · 2012
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsMetabolic syndromeMedicineEnvironmental healthPediatricsGerontologyPhysiologyInternal medicineObesity

Abstract

fetched live from OpenAlex

Background. Metabolic syndrome (MetS) includes five chronic disease risk factors which doubles the risk of CVD and increases the risk of diabetes fivefold. Objective. To determine the prevalence of MetS and its risk factors in Canadians (12-79 y) and to compare the dietary intake in Canadians with MetS and without MetS. Subjects and Methods. Cycle 1 of Canadian health measures survey, CHMS data, 2007-2009, was used. To identify MetS cases, the most recent criteria were used for adults and adolescents. Ethnical cut points for waist measurement were applied for adults. Results and Conclusion. The prevalence of MetS among 12-79 y Canadians was 18.31% with the lowest prevalence in adolescents (3.50%). Using ethnical cut points to define abdominal obesity increased the prevalence of MetS by 0.5% in adults. The most prevalent defining component of MetS in Canadians identified with MetS was abdominal obesity. Reduced HDL-C was equally prevalent among adolescents. Canadians with MetS consumed significantly more diet soft drinks, but less dairy products, dietary fat, and sugar-sweetened beverages compared to Canadians without MetS. Known cases of diabetes with MetS had healthier beverage choices compared to individuals without the diagnosis of diabetes, indicating adherence to nutrition recommendations.

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 score0.180
Threshold uncertainty score0.934

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 integrity0.0000.000
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations29
Published2012
Admission routes3
Has abstractyes

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