Pre- and within-meal effects of fluid dairy products on appetite, food intake, glycemia, and regulatory hormones in children
Bibliographic record
Abstract
The effect of beverages commonly consumed by children in-between or with meals on short-term food intake (FI) and glycemic control has received little attention. Therefore, 2 experiments were conducted in 9- to 14-year-old children following a randomized repeated-measures design. Experiment 1 (n = 32) compared the effects of water (control) and isocaloric (130 kcal) amounts of 2% milk, chocolate milk, yogurt drink, and fruit punch on subjective appetite and FI. Experiment 2 (n = 20) compared the effects of isocaloric (130 kcal) amounts of 2% milk and fruit punch on subjective appetite, FI, and glycemic and appetite hormone responses. One serving of the beverages was given as a pre-meal drink at baseline (0 min) and a second serving 60 min later with an ad libitum pizza meal. Meal FI in experiment 1 was lower by 14% and 10%, respectively, after chocolate milk and yogurt drink (p < 0.001), but not milk, compared with water. Cumulative energy intake (beverages plus meal) was higher after caloric beverages than water. In experiment 2, no differences occurred in pre-meal but post-meal glucose was 83% higher in overweight/obese than normal-weight children (p = 0.02). Milk led to higher pre-meal glucagon-like peptide-1 and post-meal peptide tyrosine tyrosine (PYY) than fruit punch (p < 0.01) but insulin did not differ between treatments. In conclusion, dairy products consumed before and with a meal have more favourable effects on FI, appetite, and satiety hormones than a sugar-sweetened beverage, but all caloric beverages result in more cumulative calories than if water is the beverage.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".