The effect of preloads of fluid milks and substitutes on short‐term food intake, appetite and glycemic response in healthy young men and women
Bibliographic record
Abstract
In a randomized, crossover design, 29 males and females (age: 22.4 ± 0.4 years, BMI: 21.9 ± 0.3 kg/m2) were provided on 5 separate occasions isovolumetric preloads (500 ml) of water (0 kcal), soy beverage (SB; 200 kcal), 2% milk (M; 260 kcal), 1% chocolate milk (CM; 340 kcal), and cow's milk-based infant formula (IF; 368 kcal) 30 min prior to an ad libitum pizza meal at which food intake was measured. Blood glucose in capillary blood samples and subjective appetite was measured at baseline and intervals pre (0–30 min) and post meal (30–170 min). Compared to water, CM, SB and IF reduced subjective appetite at 10 min (P = 0.0008). Pre-meal ingestion of only CM and IF reduced food intake by 14% (880 ± 72 kcal) and 12% (905 ± 79 kcal), respectively (P = 0.0007) compared to water (1022 ± 75 kcal). Blood glucose was higher after CM than other caloric preloads from 0 to 30 min (P < 0.0001). Post meal, both M and CM resulted in lower blood glucose at 95 min compared to water, SB and IF (P < 0.0001). Cumulative blood glucose was lower after M compared to CM consumption, but neither differed from other preloads (P = 0.005). Thus, preloads of fluid milks and substitutes differ in their effects on food intake and blood glucose. Calorie content is a primary determinant of food intake but macronutrient composition is factor in blood glucose control. Supported by Dairy Farmers of Ontario and NSERC. Grant Funding Source: Dairy Farmers of Ontario and NSERC
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 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".