Revisiting the Definition of ‘Healthy’ Participants in Substantiation of Structure/Function Claims for Dietary Supplements
Bibliographic record
Abstract
Concepts and definitions of 'healthy' have been evolving within clinical treatment algorithms as well as reference standards such as Body Mass Index and Dietary Reference Intakes. Consumers' perception of the word 'healthy' is also changing to reflect longer life span, need to stay active and in a good state of mental well-being while managing multiple diseases. Guidelines from the US Food and Drug Administration indicate that substantiating evidence for support of Structure/Function (S/F) claims for dietary supplements is best derived from clinical research conducted in a 'healthy' population. S/F claims cannot be represented to diagnose, treat, cure or prevent any disease. However, in this context, the term 'healthy' is non-descriptive and largely interpreted as an absence of disease. Guidelines for treatment of disease have been broadened to include biomarkers of disease risk such that the pool of 'healthy' volunteers eligible to be enrolled in clinical trials for S/F claim substantiation is greatly diminished. This perspective presents the challenges faced by the food and dietary supplement industry and by researcher efforts designed to substantiate S/F claims and suggest the phrase 'physiologically stable' or 'apparently healthy' as descriptions better suited to replace the term 'healthy.'
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".