Effects of added PGX<sup>®</sup>, a novel functional fibre, on the glycaemic index of starchy foods
Bibliographic record
Abstract
The development of lower-glycaemic index (GI) foods requires simple, palatable and healthy strategies. The objective of the present study was to determine the most effective dose of a novel viscous fibre supplement (PGX®) to be added to starchy foods to reduce their GI. Healthy subjects (n 10) consumed glucose sugar (50 g in water × 3) and six starchy foods with a range of GI values (52-72) along with 0 (inert fibre), 2.5 or 5 g granular PGX® dissolved in 250 ml water. GI testing according to ISO Standard 26,642-2010 was used to determine the reduction in GI. PGX® significantly reduced the GI of all six foods (P < 0.001), with an average reduction of 19 % for the 2.5 g dose and 30 % for the 5 g dose, equivalent to a reducing the GI by 7 and 15 units, respectively. Consuming small quantities of the novel functional fibre PGX®, mixed with water at the start of a meal, is an effective strategy to reduce the GI of common foods.
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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".