The effect of solid, semi‐solid and fluid snacks on food intake and satiety in children (1040.6)
Bibliographic record
Abstract
The increase in the frequency of snacking and the types of snacks consumed among children may contribute to metabolic disorders including obesity and diabetes. The objective of this study was to explore the short‐term effects of dairy and non‐dairy snacks on subjective satiety and food intake in children. Methods: In a repeated‐measures design, 23 normal weight (5th‐85th BMI percentile) children (16 girls and 7 boys; aged 9‐14 y) were randomly assigned to one of five isocaloric (180 kcal) treatments including solid foods: deep fried potato chips (32g), mini sandwich type cookies (39g), and mozzarella cheese (63g), semi‐solid: Greek yogurt (200g), and fluid: milk (2% milk fat, 346g). Following a 12hr overnight fast, participants consumed a standardized breakfast two hours prior to each study session. Subjective appetite was measured with visual analogue scale questionnaires before and at 15, 30, 45, 90, 120 and 145 min after the treatment. Food intake was measured with an ad libitum pizza meal provided at 120 min. Results: Food intake was 82 kcal lower after the cheese treatment compared to the milk treatment (P<0.05) while the other treatments led to the intermediate results. There was an effect of time (P<0.0001), but no treatment or time‐by‐treatment effect on subjective average appetite. Conclusion: A solid dairy snack (cheese) is effective in reducing food intake in children. Grant Funding Source : 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".