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Record W153294288

Mediterranean dietary components and body mass index in adults: the peel nutrition and heart health survey.

2006· article· en· W153294288 on OpenAlexaffabout
Mamdouh M. Shubair, R Stephen McColl, Rhonda M Hanning

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMedicineOverweightBody mass indexObesityMediterranean dietEnvironmental healthFood groupMarital statusDemographyGerontologyEndocrinologyPopulationInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Diet is a lifestyle factor that contributes to the risk of overweight/obesity and cardiovascular disease. The objective of this study was to examine the hypothesis that a Mediterranean-type dietary pattern (M) is associated with healthy body weights in a large suburban municipality in Ontario. A random cross-sectional sample of 759 adults, 18 to 65 years of age, participated in a telephone survey, which included questions on the frequency of consumption of 60 food categories. Principal components analysis showed that food categories aggregated into six low-order dietary factors and two high-order dietary patterns. The M pattern reflected higher consumption of fruits and vegetables, olive oil and garlic, and fish and shellfish. The non-M pattern reflected high fat/nutrient poor, meats and poultry, and foods high in added sugars. The M-score was inversely related to body mass index (BMI) (p = 0.027). After adjustment for gender, education, income and marital status, a higher M-score predicted a lower BMI in the 40 to 49 year age group. Heart health promotion strategies aimed at preventing adult obesity should emphasize components of a Mediterranean-type diet pattern.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.329
Threshold uncertainty score0.654

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.036
GPT teacher head0.261
Teacher spread0.225 · 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 source (direct Gemma or distilled Codex), 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

Citations35
Published2006
Admission routes2
Has abstractyes

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