Consumption of sugar sweetened beverages and their health impact on children
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".