Dairy snack reduces glycaemia in normal weight children
Bibliographic record
Abstract
Increased snacking in children is associated with higher energy and sugar intake, known risk factors for obesity and diabetes. The objective of this study was to determine the effect of dairy and non‐dairy snacks on glycaemia in children. Methods In a repeatedmeasures crossover design, normal weight (5 th –85 th BMI percentile) children (n =11, 5 boys and 6 girls; age: 9–14 y), were randomly assigned to consume one of two treatments: Greek yogurt (171 kcal) and mini sandwich type cookies (175 kcal). Both treatments contained 25 g of available carbohydrates. After an overnight fast, children consumed a standardized breakfast in the morning, two hours before arriving at the lab. Venous blood samples were collected for glucose and insulin at 0 min (immediately before the treatment), and at 30, 60, 90 and 120 min. Results There was an effect of treatment, time and a time by treatment interaction (P<0.0001) on blood glucose and insulin over 120 min. The yogurt treatment resulted in lower glycaemic and higher insulin responses compared to the cookies treatment (P<0.0001). This effect can be explained by the higher content of protein in the yogurt treatment (17 g) compared to the cookies treatment (1.3 g). Conclusion Macronutrient composition of a snack predetermines its glycaemic response and can affect postprandial hyperglycaemia 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".