The effect of beverage choice at an ad libitum meal caloric intake and post‐meal appetite and glycemia in healthy young men
Bibliographic record
Abstract
We hypothesized that milk with meals lowers food intake and post meal blood glucose and appetite when compared with other beverages. In a randomized crossover design, 15 men (age: 22.1±0.5 years; BMI: 22.6±0.4 kg/m2) consumed one of five beverages, 1% milk (110 kcal/250 ml), orange juice (110 kcal/250 ml), regular cola (110 kcal/250 ml), diet cola (0 kcal), and water (0 kcal), at a pizza meal. Blood glucose and subjective appetite were measured at baseline and at 20, 30, 45, 60, 75, 90, 105 and 120 min. Pizza intake was not affected by beverage, but total energy intake (P<0.0001) and postprandial appetite suppression (P=0.02) were higher with milk, orange juice and regular cola compared to water and diet cola. Ad libitum consumption of milk and pizza resulted in the highest intakes of proteins (P=0.012), vitamins and minerals and less total carbohydrate, sugars and caffeine (P<0.0001). Blood glucose area under the curve after milk was reduced by 31% and 36% compared to orange juice and regular cola, respectively (P=0.0003), with no difference between water, milk and diet cola. Glucose peaks at 30 min were lower after milk and diet cola (P<0.0001) and after only milk at 60 min (P=0.004) compared to orange juice and regular cola. While milk did not reduce meal time food intake, its contribution to nutrient balance at the meal and its lowering of post meal glycemia and appetite suggest that it is the preferred caloric meal‐time beverage. Grant Funding Source : Natural Sciences and Engineering Research Council of Canada‐Collaborative Research and Development, Dairy Farmers of Ontario 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".