Beverage Intake of Children and Youth with Obesity
Bibliographic record
Abstract
Introduction: Beverages influence diet quality, however, beverage intake among youth with obesity is not well-described in literature. Dietary pattern analysis can identify how beverages cluster together and enable exploration of population characteristics. Objectives: 1) Assess the frequency of children and youth with obesity who fail thresholds of: no sugar-sweet beverages (SSB), <1 serving/week of SSB, ≥2 servings/day of milk and factors influencing the likelihood of failing to meet these cut-offs. 2) Derive patterns of beverage intake and examine related social and behavioural factors and health outcomes at entry into Canadian pediatric weight management programs. Methods: Beverage intake of youth (2–17 years) enrolled in the CANPWR study (n=1425) was reported at baseline visits from 2013-2017. Beverage thresholds identified weekly SSB consumers and approximated Canadian recommendations. The relationship of sociodemographic (income, guardian education, race, household status) and behaviours (eating habits, physical activity, screen time) to the likelihood of failing cut-offs was explored using multivariable logistic regression. Beverage patterns were derived using Principal Component Analysis. Related sociodemographic, behavioural and health outcomes (lipid profile, fasting glucose, HbA1c, liver enzymes) were evaluated with multiple linear regression. Results: Nearly 80% of youth consumed ≥1 serving/week of SSB. This was more common in males, lower educated families and was related to eating habits and higher screen time. Two-thirds failed to drink ≥2 servings milk/day and were more likely female, demonstrated favourable eating habits and lower screen time. Five beverage patterns were identified: 1) SSB, 2) 1% Milk, 3) 2% Milk, 4) Alternatives, 5) Sports Drinks/Flavoured Milks. Patterns were related to social and lifestyle determinants; the only related health outcome was HDL. Conclusion: Many children and youth with obesity consumed SSB weekly. Fewer drank milk twice daily. Beverage intake was predicted by sex, socioeconomic status and other behaviours, however most beverage patterns were unrelated to health outcomes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".