MétaCan
Menu
Back to cohort
Record W2184896037

Diet composition and obesity among Canadian adults.

2009· article· en· W2184896037 on OpenAlexaffabout
Kellie Langlois, Didier Garriguet, Leanne Findlay

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsStatistics Canada
Fundersnot available
KeywordsObesityCalorieConfoundingOddsMedicineOdds ratioLogistic regressionNational Health and Nutrition Examination SurveyDemographyEnvironmental healthGerontologyInternal medicinePopulation
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The contribution of specific nutrients to obesity has not been definitively established. The objective of this study was to determine if an association exists between obesity and the relative percentages of fats, carbohydrates, protein and fibre in the diets of Canadians. DATA AND METHODS: The data are from the 2004 Canadian Community Health Survey--Nutrition. The analysis pertains to 6454 respondents aged 18 or older who provided valid 24-hour dietary recall information and measured height and weight, and whose reported energy intake was considered plausible based on their predicted energy expenditure. Logistic regression models with obesity status as the main outcome were conducted, controlling for potential confounders. All analyses were based on weighted estimates. RESULTS: When the effect of the control variables was taken into account, total kilocalories consumed increased the odds of obesity in men, and fibre intake decreased the odds. Among women, only total kilocalories consumed was significantly associated with increased odds of obesity. INTERPRETATION: Higher consumption of kilocalories increased the odds of obesity, but the relative amounts of fats, carbohydrates and protein were generally not significant. The sole exception was an association between higher fibre intake and lower rates of obesity among men.

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 score0.233
Threshold uncertainty score0.996

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 head0.010
GPT teacher head0.204
Teacher spread0.193 · 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

Citations34
Published2009
Admission routes2
Has abstractyes

Explore more

Same venuePubMedSame topicDiet and metabolism studiesFrench-language works237,207