Effects of a healthy meal course on spontaneous energy intake, satiety and palatability
Bibliographic record
Abstract
Many food components can influence satiety or energy intake. Combined together, these food components could represent an interesting dietary strategy in the prevention and treatment of obesity. The aims of this study were: 1) to determine the effect of a functional food in the form of a healthy meal course on subsequent energy intake and satiety; 2) to verify if it is possible to maintain palatability while preserving the satiating effects of the test meal. Thirteen subjects were invited to eat two lunch sessions: healthy and control meal courses (2090 kJ/meal). Anthropometric and ad libitum food intake measurements, and visual analogue scales (VAS) were performed during the two lunch sessions. The healthy main course acutely decreased energy intake during the rest of the meal ( − 744 kJ, P ≤ 0·0001) and lipid ( − 6 %, P ≤ 0·0001) compared with the control meal. VAS ratings during the course of the testing showed a meal effect for hunger, desire to eat and prospective food consumption (P ≤ 0·05) and a time effect for all appetite sensations (P ≤ 0·0001). VAS scores on hunger ratings were lower for the healthy meal (P ≤ 0·05), whereas fullness ratings were higher shortly after the healthy main course (P ≤ 0·05). The healthy meal produced a slightly higher palatability rating but this effect was not statistically significant. These results suggest that it is possible to design a healthy meal that decreases spontaneous energy intake and hunger without compromising palatability.
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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) | 0.003 | 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".