Prebiotics in food and dietary supplements: a roadmap to EU health claims
Bibliographic record
Abstract
Numerous studies have established that prebiotic ingredients in foods and dietary supplements may play a role in supporting human health. Over the three decades that have passed since prebiotics were first defined as a concept, research has revealed a complex universe of prebiotic-induced changes to the human microbiota. There are strong indications of a direct link between these prebiotic-induced changes and specific health benefits. However, at the present time, the EU has not permitted use of the term ‘prebiotic’ in connection with an approved health claim. This paper is the outcome of a workshop organized on the 25th October 2023 by the European branch of the International Life Science Institute (ILSI). It provides an overview of the regulatory requirements for authorized health claims in the EU, key areas of prebiotic research, and findings to date in relation to prebiotics and digestive, immune, metabolic and cognitive health. Research gaps and documentation challenges are then explored and a roadmap proposed for achieving authorization of ‘prebiotic’ in the wording of future EU health claims.
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 | 0.072 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.020 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".