The effect of dairy products consumed with high glycemic carbohydrate on subjective appetite, food intake, and postprandial glycemia in older adults
Bibliographic record
Abstract
The objective was to compare the effect of liquid, semi-solid, and solid dairy products and a nondairy beverage when consumed with glycemic carbohydrate on subjective appetite, food intake (FI), and post-prandial glycemia (PPG) in healthy older adults. Thirty healthy men and women (14 males and 16 females; age: 64.6 ± 2.4 y; BMI: 25.6 ± 2.5 kg/m2) participated in a randomized crossover study. Treatments were one of 250 mL of 2% fat milk and soy beverage, 175 g of 2% Greek yogurt, and 30 g of Cheddar cheese consumed as part of an isocaloric (380 kcal) meal with bread and jam. Water alone served as the energy-free control for subjective appetite. At 180 min after consumption, the participants were fed an ad libitum meal to measure FI. Subjective appetite, blood glucose, and insulin were measured at baseline and at intervals both before (post-treatment) and after the meal (postmeal). Cheese and yogurt resulted in lower post-treatment blood glucose than milk and soy beverage when consumed with carbohydrate (p < 0.0001), but no differences among any treatments were observed postmeal. Treatments led to similar insulin concentrations. Post-treatment appetite was lower than after the water control for all treatments but suppressed more by cheese and yogurt compared with milk (p < 0.0001). There were no differences in FI among treatments. Cheese and yogurt increase satiety and lower PPG more than milk or a soy beverage when consumed with carbohydrate.
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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 | 0.001 | 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".