The effect of before meal consumption of fluid milks and substitutes on short‐term food intake, appetite and glycemic response in healthy young men and women
Bibliographic record
Abstract
We hypothesized that consuming milk before and between meals compared with substitutes or orange juice is better for appetite and glycemic control. Two experiments compared the effect of consuming at 30 (Experiment 1) or 120 min (Experiment 2) before a pizza meal, isovolumetric (500 ml) amounts of water, soy beverage (SB; 200 kcal), 2% milk (M; 260 kcal), 1% chocolate milk (CM; 340 kcal), orange juice (OJ; 229 kcal) and cow's milk‐based infant formula (IF; 368 kcal) on appetite, food intake (FI) and blood glucose (BG) before and after a meal in healthy young men and women. Compared to water, all preloads reduced pre‐meal appetite (P = 0.0008). Pre‐meal ingestion of CM and IF reduced FI by 14% (880 ± 72 kcal) and 12% (905 ± 79 kcal), respectively (P = 0.0007) compared to water (1022 ± 75 kcal) at 30 min, but no preloads reduced FI at 2 h. Blood glucose was higher after CM than other caloric preloads from 0 to 30 min and after CM and OJ from 0 to 120 min (P < 0.0001). Only M reduced post‐meal BG in both experiments (P < 0.0001) and its effects were independent of meal time energy intake. Overall pre‐ and post‐meal BG was lower after M than after CM and OJ, but did not differ from water or IF (P = 0.005). Thus, pre‐meal consumption of 2% milk provided better glycemic response than other beverages, however, calorie content and inter‐meal intervals were primary determinants of their effects on FI. Grant Funding Source : Dairy Farmers of Ontario, Natural Sciences and Engineering Research Council of Canada (NSERC) and Kraft 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 | 0.000 |
| 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".