Mealtime beverage and food intake to satiation interacts with meal advancement in healthy young men and women
Bibliographic record
Abstract
Thirst, hunger, eating and drinking are closely interrelated. However, their interaction during meal progression and the impact of beverage type on this interrelationship have not been reported. In a randomized controlled study, 29 men and women (22 ± 0.4 years; 22 ± 0.3 kg/m 2 ) consumed to satiation a pizza meal with one of water, 1% milk, regular cola, orange juice and diet cola. Mealtime food and fluid intake were measured within each of three 7‐min phases of the meal. A progressive decline occurred from phase 1 to 3 in fluid intake, averaging 59 ml, and food intake, averaging 268 kcal (P < 0.0001); however, the relative intake of fluid to food increased (P < 0.0001). Beverage type did not alter the overall association of fluid and food intake with meal progression. However, the effect of beverage type on fluid and food intake changed by phases of the meal. Although pizza intake was similar with all beverages averaging 932 kcal, the amount of fluid consumed was higher with orange juice than diet cola during phases 1 (P = 0.01) and 2 (P < 0.001) of the meal. Caloric beverages led to higher mealtime total energy intake compared to water (P < 0.001) and diet cola (P < 0.0001), during all three phases of the meal. Baseline thirst, but not appetite, correlated with fluid (r = 0.277; P = 0.001) and food (r = 0.163; P = 0.050) intake. In conclusion, meal progression was a major determinant of the relationship between mealtime beverage and food intake. Supported by Natural Sciences and Engineering Research Council of Canada‐Collaborative Research and Development, Dairy Farmers of Ontario and Kraft Canada Inc.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".