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Food Away from Home, Sugar-Sweetened Drink Consumption and Juvenile Obesity

2003· article· en· W2078120936 on OpenAlexaffabout
Linda Gillis, Oded Bar‐Or

Bibliographic record

VenueJournal of the American College of Nutrition · 2003
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsHamilton Health Sciences
Fundersnot available
KeywordsObesityMedicineSugarCalorieAdded sugarBioelectrical impedance analysisEnvironmental healthFood away from homeDietary SucroseFood groupRefined grainsFood scienceBody mass indexFood consumptionWhole grainsEndocrinologyBiology

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify if particular foods or food groups may be associated with obesity in children and adolescents and to determine if consuming food away from home (FAFH) has an effect on the nutritional quality of their diets. DESIGN: One-year cross-sectional study. SETTING/SUBJECTS: The obese subjects (n = 91) were on the waiting list for a hospital-based weight control treatment program. The non-obese subjects (n = 90) were recruited from community advertisements. MEASURES OF OUTCOME: Information on food intake was obtained using the dietary history method by a Registered Dietitian. Body fat was determined by bioelectrical impedance analysis. RESULTS: Obese children and adolescents consumed significantly more servings of meat and alternatives, grain products, FAFH, sugar-sweetened drinks and potato chips which contributed to a higher calorie, fat and sugar intake compared to non-obese children and adolescents. Sugar-sweetened drinks were only significantly greater in boys. The consumption of meat servings, sugar-sweetened drinks and FAFH was positively correlated with percent body fat. The frequency of food consumed outside of the Canada's Food Guide To Healthy Eating was not different between the two groups. CONCLUSIONS: Obese children and adolescents need to limit their access to food consumed away from home and sugar-sweetened drinks as there is a relationship between these foods and body fatness.

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 score0.065
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.247
Teacher spread0.233 · 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

Citations216
Published2003
Admission routes2
Has abstractyes

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