A perspective on phenolic compounds, their potential health benefits, and international regulations: The revised Brazilian normative on food supplements
Bibliographic record
Abstract
Phenolic compounds possess a myriad of health benefits, thus making them potential ingredients for the procurement of food supplements. Anvisa, Brazil’s national regulatory body, has recently revised the regulation on food supplements and only a few phenolic compounds (chlorogenic acid, rutin, proanthocyanidins, and tocopherols) have been mentioned. Despite several scientific evidences on the bioactivity of phenolics, especially concerning their antioxidant activity, Anvisa does not authorize any claims for supplements containing these compounds, except for tocopherols, which was mentioned as “vitamin E”. The upper limit doses allowed for the phenolics appear to be lower than what the literature suggests as necessary to achieve potential health benefits and might be prohibitive for supplements available in the international market. Moreover, Brazilian sources of phenolic compounds are not listed in the Normative Instruction (NI) as authorized ingredients for supplements. The Brazilian NI on food supplements has moved forward, but it is still limited.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".