Impact of oat processing on glycaemic and insulinaemic responses in healthy humans: a randomised clinical trial
Bibliographic record
Abstract
Oats can be processed in a variety of ways ranging from minimally processed such as steel-cut oats (SCO), to mildly processed such as large-flake oats (old fashioned oats, OFO), moderately processed such as instant oats (IO) or highly processed in ready-to-eat oat cereals such as Honey Nut Cheerios (HNC). Although processing is believed to increase glycaemic and insulinaemic responses, the effect of oat processing in these respects is unclear. Thus, we compared the glycaemic and insulinaemic responses elicited by 628 kJ portions of SCO, OFO, IO and HNC and a portion of Cream of Rice cereal (CR) containing the same amount of available-carbohydrate (23 g) as the oatmeals. Healthy males (n 18) and females (n 12) completed this randomised, cross-over trial. Blood was taken fasting and at intervals for 3 h following test-meal consumption. Glucose and insulin peak-rises and incremental AUC (iAUC) were subjected to repeated-measures ANOVA using Tukey's test (two-sided P<0·05) to compare individual means. Glucose peak-rise (primary endpoint, mean (sem) mmol/l) after OFO, 2·19 (sem 0·11), was significantly less than after CR, 2·61 (sem 0·13); and glucose peak-rise after SCO, 1·93 (sem 0·13), was significantly less than after CR, HNC, 2·49 (sem 0·13) and IO 2·47 (sem 0·13). Glucose iAUC was significantly lower after SCO than CR and HNC. Insulin peak rise was similar among the test meals, but insulin iAUC was significantly less after SCO than IO. Thus, the results show that oat processing affects glycaemic and insulinaemic responses with lower responses associated with less processing.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".