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Record W7067726708

Modifications in food-group consumption are related to long-term body-weight changes

2017· other· en· W7067726708 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicYeasts and Rust Fungi Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWaistObesityConsumption (sociology)Food consumptionBody weightFood intakeFood groupBody fat percentage
DOInot available

Abstract

fetched live from OpenAlex

Background: Dietary patterns play an important role in the control of body weight.\nObjective: The aim of this study was to verify whether changes in some dietary patterns over a 6-y follow-up period would be associated with weight changes.\nDesign: A sample of 248 volunteers of the Québec Family Study were measured twice (visit 1: 1989–1994; visit 2: 1995–2000). Body weight, percentage body fat, subcutaneous skinfold thicknesses, and waist circumference measurements as well as 3-d dietary and physical activity records were obtained at each visit. At visit 2, all participants filled out a food-based questionnaire examining changes in the consumption of 10 food categories. To further investigate the relation between changes in food-group consumption and bodyweight changes, a total of 51 food subcategories were identified from dietary records.\nResults: A self-reported decrease in the consumption of food in the fat group or an increase in consumption in the fruit group from the food-based questionnaire predicted a lower increase in body weight and adiposity indicators over time. A more detailed examination of the change in food groups between diet records revealed that increases in the consumption of whole fruit as well as skimmed milk and partly skimmed milk were the 2 food patterns that negatively correlated with the changes of each body weight–related indicator. \nConclusions:These results show that changes in the consumption of some specific food groups are associated with body-weight changes. Such specific eating patterns could help to improve obesity treatment and prevention.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.072
Threshold uncertainty score0.771

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 head0.022
GPT teacher head0.277
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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