Liquid and solid sources of added sugar and their associations with body weight and metabolic syndrome components in children
Bibliographic record
Abstract
Limited evidence exists in children relating added sugar (ASug) from solid‐food sources with obesity and metabolic syndrome (MetSyn) in contrast to considerable data on sugar‐sweetened beverages (SSB). Our aim was to examine dietary sources of ASug and their relationship with body weight indicators and MetSyn components. Exposure data were three 24‐hour dietary recalls from 613 Caucasian children (8–10 y) in the QUebec Adiposity and Lifestyle InvesTigation in Youth (QUALITY) study (2008–2010). Outcome measurements included body mass index (BMI), waist circumference (WC), waist‐to‐height ratio (WHR), triglyceride, HDL‐C, systolic blood pressure (SBP) and HOMA‐IR. Multivariate regressions were used with covariates of age, sex, fat mass index (fat mass by dual‐energy absorptiometry) and physical activity (7‐day accelerometer). On average, 12% of energy intake (204 kcal) came from ASug. Four top sources of ASug were sweets (contributed 28% of ASug), baked products (25.9%), SSB (17.1%) and ready‐to‐eat cereal (5.6%). No associations were found with overweight or metabolic indicators with solid‐food sources of ASug. This was in contrast to the association of ASug from SSB with BMI, WC, WHR and SBP. Although children consumed more ASug from sweets and baked products than from SSB, these solid sources of ASug were not associated with health indicators. Grant Funding Source : Canadian Institutes of Health Research, Heart and Stroke Foundation of Canada and Fonds de la recherche en sante du Quebec.
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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 | 0.000 |
| 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 | 0.001 |
| 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".