Effect of Commercially Available Sugar-Sweetened Beverages on Subjective Appetite and Short-Term Food Intake in Boys
Bibliographic record
Abstract
It is unclear whether sugar sweetened beverages bypass regulatory controls of food intake (FI) in boys. The objective of the present study was to determine the effects of isovolumetric preloads (350 mL) of a fruit-flavoured drink (154 kcal), cola (158 kcal), 1% M.F. chocolate milk (224 kcal), and water (0 kcal) on subjective appetite and FI in boys aged 9–14 years. On four separate mornings, boys consumed one of the preloads in a random order; subjective appetite was measured at 15 min intervals, and FI was measured via an ad libitum pizza lunch at 60 min post-beverage consumption. In the 32 boys (age: 11.8 ± 0.3 years), FI was reduced (p < 0.001) after cola (940 ± 46 kcal) and chocolate milk (878 ± 41 kcal) compared with the water control (1048 ± 35 kcal) and after chocolate milk compared to the fruit drink (1005 ± 44 kcal). Cumulative FI after the fruit drink was greater than the water control (1159 ± 44 vs. 1048 ± 35 kcal; p = 0.03). Average appetite was not affected by the treatment, but the cola treatment resulted in greater fullness (p = 0.04) and lower prospective food consumption (p = 0.004) compared with the fruit drink. In conclusion, chocolate milk and cola suppressed next-meal FI at 60 min, while fruit drink increased cumulative FI (beverage + next meal) over 60 min in boys. Results from this study suggest that beverage composition is an important determinant of FI suppression in boys.
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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) | 0.001 | 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) | 0.002 | 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".