Comparison of the effects of Chemical Composition, Processing and Food Form on the Satiety of Barley
Bibliographic record
Abstract
Low glycemic index (GI) diets have been promoted for weight maintenance due to their effect on satiety. Barley, a low‐GI cereal has been suggested as a satiety inducing food. Recently a number of barley cultivars have been developed for consumer uses and it has been shown that differences in chemical composition, food processing and food‐form affect glycemic responses, but the effect on satiety is not known. To investigate these factors nine cultivars varying in the nature of starch and β‐glucan were studied in two experiments in separate groups of 10 subjects. Satiety was measured by visual analog scales over two hours, satiety area under curve (AUC) and satiety index (SI) were calculated. Experiment 1: seven cultivars were tested with one undergoing four levels of pearling ranging from Whole Grain (WG) to White Pearled (WP). There were no differences in satiety AUC among cultivars or compared to white bread (WB) (AUC WB = 4774 ± 478 mm vs. AUC highest cultivar = 7518 ± 564 mm, P = 0.45) nor differences in SI (P = 0.77). Similarly pearling did not have an effect on satiety (P = 0.99). Experiment 2: WG and WP of 2 cultivars varying in total fiber were made into wet pasta. Compared to WB, high fiber barley pasta had a higher satiety AUC (P = 0.007) but not the low fiber barley pasta (P = 0.33). In conclusion, Food form may affect satiety; however these results do not support the hypothesis that inducing satiety is part of glycemic‐index mechanism. Grant Funding Source : CHIR
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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 | 0.000 |
| 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".