Dietary sugar intake among preschool-aged children: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Excessive intake of sugar in young children is a public health concern. Our study objectives were to examine intakes of total, free and added sugar among preschool-aged children and to investigate their associations with body weight, body mass index Z-scores, percent fat mass and waist circumference. METHODS: The cross-sectional cohort study included preschool-aged children between 1.5 and 5 years of age, enrolled in pilot studies of the Guelph Family Health Study, Guelph, Ontario, from 2014 to 2016. Daily intake of total sugar was determined using a food processor software; daily intakes of free and added sugar, and food sources were determined through manual inspection of 3-day food records. Anthropometric measures were completed by trained research staff. We used linear regression models with generalized estimating equations to estimate associations between sugar intakes and anthropometric measures. RESULTS: = 87) had intakes of free sugar greater than 5% of their daily energy intake. The most frequent food sources of free and added sugar were bakery products. A weak inverse association between free sugar intake (kcal/1000 kcal) and waist circumference (cm) (β = -0.02, 95% confidence interval -0.04 to -0.0009) was found, but no significant associations were noted between sugar intake and other anthropometric measures. INTERPRETATION: Most of the preschool-aged children in this study had free sugar intakes greater than current recommendations; overall, their total, free and added sugar intakes were not associated with the anthropometric measures. This study can be used to inform policy development for sugar intake in young children and apprise early intervention programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".