Effect of 100% Orange Juice and a Volume-Matched Sugar-Sweetened Drink on Subjective Appetite, Food Intake, and Glycemic Response in Adults
Bibliographic record
Abstract
Dietary recommendations to reduce the consumption of free sugars often group 100% fruit juice with other sugar-containing beverages. The objective of this study was to determine the effect of consuming 100% orange juice compared to an orange drink on next-meal food intake (FI), glycemic response, average appetite, emotions, and sensory characteristics in normal-weight adults. Thirty-six normal-weight adults (age: 26.8 ± 0.9 years) consumed, in random order and at least 5 days apart, three 240 mL test beverages as follows: (a) 100% orange juice, (b) orange drink, or (c) water. Subjective sweetness and pleasantness were determined immediately after test beverage consumption. Glycemic response, average appetite, and subjective emotions were measured every 15 min for 60 min. Food intake was determined at a pizza lunch 60 min later. Rest-of-day glycemic response and energy intake (EI) were determined using a continuous glucose monitor and food record, respectively. Lunch FI (p = 0.054) and total EI (p = 0.01) were both lower after 100% orange juice compared with the orange drink. Caloric compensation was 84% after 100% orange juice and −25% after the orange drink (p = 0.047). Average appetite was not significantly different between the test beverages (p > 0.05). Blood glucose iAUC adjusted for available carbohydrate was lower after 100% orange juice compared with the orange drink (p < 0.001). Rest-of-day blood glucose concentrations were lower after 100% orange juice compared with the orange drink (p = 0.03) and water control (p < 0.001). In conclusion, consumption of 100% orange juice as a preload resulted in higher caloric compensation, lower total daily EI, and lower blood glucose concentrations compared to the orange drink.
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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".