Chia Seed (Salvia hispanica L.), Incorporated Into Cookies, Reduces Postprandial Glycemic Variability but Has Little or No Ef-Fect on Subjective Appetite
Bibliographic record
Abstract
Chia seed, high in dietary-fiber and protein, may have potential to increase satiety. However, previous studies are confounded by differences in energy intake, and none investigated doses <7g. We compared the subjective appetite and glycaemic responses elicited by 30g cookies containing 0, 3, 5 or 7g chia seed (CS0, CS3, CS5 or CS7, respectively, 140-150kcal, 7-8g fat, 4g protein, 0-2g dietary-fiber, 16g available-carbohydrate) using a randomized, double-blind, cross-over design. Overnight-fasted heathy adults (24 males, 22 females; mean±SD age 37±13y; BMI 27.4±3.9kg/m²) consumed test-cookies and had appetite and plasma-glucose measured before and intermittently for 3h after eating. Data were analyzed by ANOVA with Tukey’s test to adjust for multiple comparisons; the criterion for significance was 2-tailed p<0.025 for the primary endpoints of total area under the curve (tAUC0-3h) for hunger and fullness. Hunger tAUC0-3h was similar among treatments (p=0.49) but fullness differed (p=0.019) with tAUC0-3h after CS3>CS7 (mean±SEM) (140±9 vs 122±10mm×h, p<0.025). Mean incremental glucose AUC0-2h after CS3, CS5 and CS7, respectively, were 22%, 23% and 30% less than CS0 (p<0.05). Thus, although chia reduced glycemic responses, we were unable to demonstrate a significant effect of ≤7g chia seed on the primary endpoints of hunger or fullness tAUC0-3h versus control.
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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 | 0.001 |
| 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".