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Record W6980743684

Consumption of sugar sweetened beverages and their health impact on children

2021· article· en· W6980743684 on OpenAlexaboutno aff

Bibliographic record

VenueJMU Scholoraly Commons (James Madison University) · 2021
Typearticle
Languageen
FieldMedicine
TopicBone and Dental Protein Studies
Canadian institutionsnot available
Fundersnot available
KeywordsConsumption (sociology)CalorieHealth benefitsObesityPublic healthLongitudinal studyIntervention (counseling)
DOInot available

Abstract

fetched live from OpenAlex

Consumption of sugar-sweetened beverage is a major contributor to sugar-based calories in the daily diet of many children. Children (up to 18 years) have different nutritional needs and metabolic pathways than adults. Although many studies explored the health effects of sugar-sweetened beverages among adults, few studies included children in their analysis. The purpose of this review was to evaluate and summarize the current global trends in the consumption of sugar-sweetened beverages and the health effects of consumption of sugar-sweetened beverages in children. The review identified several health effects related to children’s sugar-sweetened beverage consumption, such as childhood obesity, metabolic syndrome, early menarche, and dental caries. A decline in children’s Consumption of sugar-sweetened beverages was noted in Australia, Canada, Norway, USA, and UK between 2000-2010 but increased in countries such as Mexico and South Korea, and the trend remained stable in China and Russia. Several influencing factors for children’s sugar-sweetened beverage consumption were identified, including parents' perception and attitude towards sugar-sweetened beverage, Children’s gender differences, and socio-economic status (SES). More longitudinal studies are required to determine the cause-effect relationship between sugar-sweetened beverage consumption and the reported health effects. Researchers should also consider the influence of social and behavioral factors identified in this review when planning intervention programs for children.

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 score0.028
Threshold uncertainty score0.607

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 head0.021
GPT teacher head0.274
Teacher spread0.253 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueJMU Scholoraly Commons (James Madison University)Same topicBone and Dental Protein StudiesFrench-language works237,207