Differential Serving Sizes of High β Glucan Oatmeal Elicit Lower Glycemic Response than Rice Cereal
Bibliographic record
Abstract
To determine impact of serving size and cereal type along with addition of sugar on the glycemic response (GLR) elicited by oatmeal, we compared the GLR over 2 hr elicited by 30, 40 and 60g high β glucan (5.2%) Classic Quaker Quick Oats (18, 24 and 36g available carbohydrate (CHO), respectively) and by 30g Oats plus 9g sugar (sucrose) with those after available CHO matched portions (22, 29, 44 and 33g, respectively) of Cream of Rice cereal (Control) in 38 healthy subjects (mean±SD age 40±12 yr, BMI 26.4±3.6 kg/m²). As serving size increased, incremental area under the curve (AUC), peak rise (PR), rate of decline (RD) and time to baseline (Tb) for blood glucose increased significantly for both cereals. Time to peak (Tp) tended to increase with serving size (significant only for Oats). Overall mean AUC, PR and RD, respectively for oats without sugar were 24%, 25%and 24% less than those after Control (all p<0.05). The GLR after Oats plus sugar was compared to that after 33g Control without sugar (both test meals contained 27g available CHO). After Oats plus sugar AUC, PR and RD were significantly lower than after 33g Control without sugar by 29%, 15% and 23%, respectively. It is concluded that high β glucan Oatmeal elicits a lower glycemic response than Cream of Rice cereal. Measures of GLR tended to increase with increased serving size but the rate of change of some of the measures varied for the different cereals. Adding sugar to Oatmeal elicits a significantly lower GLR than a serving of Cream of Rice cereal containing the same amount of available carbohydrate.
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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".