The Evolution of Science and Regulation of Dietary Supplements: Past, Present, and Future
Bibliographic record
Abstract
Dietary supplement use in the United States is widespread and increasing, especially among certain population groups, such as older Americans. The science surrounding dietary supplements has evolved substantially over the last few decades since their formal regulation in 1994. Much has been learned about the mechanisms of action of many dietary supplement ingredients, but the evidence on their health effects is still building. As is true of much nutrition research, there are many studies that point to health effects, but not all are at the level of scientific evidence (e.g., randomized controlled interventions), rigor, or quality needed for definitive statements of efficacy regarding clinical end points. New technologies and approaches are being applied to the science of dietary supplements, including nutrigenomics and microbiome analysis, data science, artificial intelligence (AI), and machine learning-all of which can elevate the science behind dietary supplements. Products can contain an array of bioactive compounds derived from foods as well as from medicinal plants, which creates enormous challenges in data collection and management. Clinical applications, particularly those aimed at providing personalized nutrition options for patients, have become more sophisticated as dietary supplements are incorporated increasingly into clinical practice and self-care. The goals of this article are to provide historical context for the regulation and science of dietary supplements, identify research resources, and suggest some future directions for science in this field.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".