Systematic review of the effect of processing of whole-grain oat cereals on glycaemic response
Bibliographic record
Abstract
Whole-grain oats have been identified as a type of food that blunts blood glucose increase after a meal. However, processing of oats changes the physical characteristics of the grain, which may influence human glycaemic response. Therefore, the effect of different processes on acute postprandial glycaemic response, quantified using glycaemic index (GI) measurements, was investigated in a systematic review. A review of the literature identified twenty publications containing fifty-six individual tests. An additional seventeen unpublished tests were found in an online database. Of the seventy-two measurements included in the review, two were for steel-cut oats, eleven for large-flake oats, seven for quick-cooking (small flake) oats, nine for instant oatmeal and twenty-eight for muesli or granola. One granola measurement was identified as an outlier and was removed from the statistical analysis. In all, fifteen clinical tests were reported for rolled oat porridge that did not specify the type of oats used, and thus the effect of processing could not be assessed. Steel-cut oats (GI=55 (se 2·5)), large-flake oats (GI=53 (se 2·0)) and muesli and granola (GI=56 (se 1·7)) elicited low to medium glycaemic response. Quick-cooking oats and instant oatmeal produced significantly higher glycaemic response (GI=71 (se 2·7) and 75 (se 2·8), respectively) than did muesli and granola or large-flake oatmeal porridge. The analysis establishes that differences in processing protocols and cooking practices modify the glycaemic response to foods made with whole-grain oats. Smaller particle size and increased starch gelatinisation appear to increase the glycaemic response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".