The effect of fluid dairy products on food intake, glycemic and appetite hormone responses in children (120.3)
Bibliographic record
Abstract
Dairy intake has been linked to healthier body weight (BW) in children, but the effect of milk consumption on short‐term food intake (FI) has not been explored in this group. We examined the effect of milk compared to other commonly consumed drinks on subjective appetite (SA), FI, glycemia and appetite hormones (AH) in normal weight (NW) and overweight/obese (OWOB) children in two experiments. Exp. 1 compared the effects of pre‐meal and within meal consumption of isocaloric (130 kcal) and isovolumetric (250 mL) drinks of 2% milk, 1% chocolate milk (CM), 1.5% yogurt drink (YD), fruit punch (FP) and water on SA and FI at a pizza meal given 60 min later in NW and OWOB children. In all children, FI was 13 and 9% lower after CM and YD, respectively ( P < 0.01), than water. Post‐meal SA was lower after milk than YD ( P < 0.01). Exp. 2 compared the effects of pre‐meal and within meal consumption of milk and FP on SA, FI 60 min later, glucose and AH responses in NW and OWOB children. Pre‐meal SA increased and post‐meal SA decreased more after milk than FP ( P < 0.05). Meal‐time FI and glucose and insulin AUCs were similar after the drinks and not affected by BW, but AH responses were altered. GLP‐1 AUC was higher after milk than FP and in OWOB compared to NW children ( P < 0.03). The post‐prandial drop in ghrelin was greater after milk than FP in OWOB children (‐24 vs. ‐14%, P = 0.06), but similar in NW children after both drinks (‐12 vs. ‐16%, P = 0.58). Thus, pre‐meal and within meal milk consumption stimulates GLP‐1 and leads to greater ghrelin suppression in OWOB children, with no effect on FI. Grant Funding Source : Supported by Dairy Farmers of Canada
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".