A premeal snack of raisins decreases mealtime food intake more than grapes in young children
Bibliographic record
Abstract
The effect of a premeal snack of grapes, raisins, or a mix of almonds and raisins, compared with a water control, on food intake (FI) was examined in 8- to 11-year-old normal-weight (15th to 85th percentile) children. Children randomly received 1 of 4 ad libitum (Experiment 1: 13 boys, 13 girls) or fixed-calorie (150 kcal; Experiment 2: 13 boys, 13 girls) treatments, followed by an ad libitum pizza meal 30 min later. Appetite was measured throughout the study, and FI was measured at 30 min. The ad libitum consumption (Experiment 1) of raisins reduced pizza intake (p < 0.037), compared with water (26%), grapes (22%), and the mixed snack (15%). Cumulative energy intake (in kcal: snack + pizza) was lower after water and raisins than after either grapes or the mixed snack (p < 0.031). As a fixed-calorie (150 kcal) snack (Experiment 2), raisins reduced pizza intake, compared with water (∼11%, p = 0.005), and resulted in a cumulative intake similar to water; however, both grapes and the mixed snack resulted in higher cumulative intakes (p < 0.015). Appetite was lower after all caloric ad libitum snacks (p < 0.003) and after fixed amounts of grapes and the mixed snack (p < 0.037), compared with water. In conclusion, consumption of a premeal snack of raisins, but not grapes or a mix of raisins and almonds, reduces meal-time energy intake and does not lead to increased cumulative energy intake in children.
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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 | 0.001 |
| 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".