World trends in sugar-sweetened beverage and dietary sugar intakes in children and adolescents: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To provide a systematic overview of world dietary sugar and sugar-sweetened beverage (SSB) intake trends in children and adolescents. DATA SOURCES: Medline, Embase, and the Cochrane Central Register of Controlled Trials in the Cochrane Library were searched through January 2019 to identify longitudinal follow-up studies with time-trend data and repeated cross-sectional studies. DATA EXTRACTION: Data from studies reporting ≥ 2 measurements (sugars, SSB, or sweets/candy) over ≥ 2 years and included ≥ 20 healthy, normal- or overweight children or adolescents aged 1-19 years. DATA ANALYSIS: Data from 43 articles (n = 4 prospective cohort studies; n = 39 repeated cross-sectional studies) from 15 countries (n = 8 European countries plus Australia, Canada, China, South Korea, Mexico, Russia, and the United States) are presented narratively. According to the risk of bias in nonrandomized studies of interventions tool, 34 studies were judged to have a moderate risk of bias, and 5 to have a serious risk of bias. CONCLUSIONS: Consumption among US children and adolescents increased substantially in the decades preceding 2000, followed by a faster and continued decline. As a whole, other international intake trends did not reveal drastic increases and decreases in SSB and dietary sugars; they tended to change only slightly across 3 decades.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".