Effect of Sugars‐Containing Beverages on Satiety and Short‐Term Food Intake in Normal Weight and Overweight/Obese Girls
Bibliographic record
Abstract
The effect of commercial beverages on satiety and short‐term food intake (FI) has received little investigation in girls. The purpose of this study was to determine the effect of commonly consumed sugars‐containing beverages on FI regulation in normal weight (NW) (n=12; 15–85 th BMI percentile) and overweight (OW)/obese (OB) girls (n=11; >;85 th BMI percentile). On 4 separate mornings and in random order, girls (n=23) received 350 ml of either a fruit drink, carbonated cola, 1% chocolate milk or a water control 2 h after a standardized breakfast of milk, cereal, and orange juice. FI (mean kcal ± SEM) from an ad libitum pizza meal was measured 60 min later. Only 1% chocolate milk decreased FI (746 ± 64) compared with the water control (935 ± 64; P<0.001) in NW girls, but none of the beverages decreased FI in OW/OB girls. In the pooled sample (n=23), FI was decreased by carbonated cola and 1% chocolate milk compared with the water control, but the fruit drink failed to result in a statistically significant decrease in FI. Caloric compensation scores for the fruit drink, cola, and 1% chocolate milk were 76%, 80%, and 86% in NW, and 45%, 74%, and 59% in OW/OB girls, respectively. Prospective food consumption (P<0.05) and desire‐to‐eat (P<0.05) scores, when corrected for the energy content of the beverages, were lowest after 1% chocolate milk compared to both cola and fruit drink, and cola, respectively. In conclusion, the effect of sugars‐containing beverages on short‐term FI in girls was dependent on the interaction between macronutrient composition and body weight status.
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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".