ILSI Brazil International Workshop on Functional Foods: a narrative review of the scientific evidence in the area of carbohydrates, microbiome, and health
Bibliographic record
Abstract
To stimulate discussion around the topic of 'carbohydrates' and health, the Brazilian branch of the International Life Sciences Institute held the 11th International Functional Foods Workshop (1-2 December 2011) in which consolidated knowledge and recent scientific advances specific to the relationship between carbohydrates and health were presented. As part of this meeting, several key points related to dietary fiber, glycemic response, fructose, and impacts on satiety, cognition, mood, and gut microbiota were realized: 1) there is a need for global harmonization of a science-based fiber definition; 2) low-glycemic index foods can be used to modulate the postprandial glycemic response and may affect diabetes and cardiovascular outcomes; 3) carbohydrate type may influence satiety and satiation; glycemic load and glycemic index show links to memory, mood, and concentration; 4) validated biomarkers are needed to demonstrate the known prebiotic effect of carbohydrates; 5) negative effects of fructose are not evident when human data are systematically reviewed; 6) new research indicates that diet strongly influences the microbiome; and 7) there is mounting evidence that the intestinal microbiota has the ability to impact the gut-brain axis. Overall, there is much promise for development of functional foods that impact the microbiome and other factors relevant to health, including glycemic response (glycemic index/glycemic load), satiety, mood, cognition, and weight management.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".