Nutritional Supplements and Chronic Disease: Bridging the Gap between Myth and Reality
Bibliographic record
Abstract
Abstract Several studies have appraised the role of vitamin and mineral supplements in primary or secondary prevention of chronic disease have consistently found null results or possible harms. Evidence involving tens of thousands of people randomly assigned in many clinical trials shows that β-carotene, vitamin E, and possibly high doses of vitamin A supplements increase mortality and that other antioxidants, folic acid, B and D vitamins, and multivitamin supplements have no clear benefit. The large body of accumulated evidence has important public health and clinical implications. Evidence is sufficient to advise against routine supplementation, and we should translate null and negative findings into action. The message is simple: most supplements do not prevent chronic disease or death, their use is not justified, and they should be avoided. This message is especially true for the general population with no clear evidence of micronutrient deficiencies, who represent most supplement users in the United States and in other countries. The evidence also has implications for research. Antioxidants, folic acid, and B vitamins are harmful or ineffective for chronic disease prevention, and further large prevention trials are no longer justified. Vitamin D supplementation, however, is an open area of investigation, particularly in deficient persons. However, clinical trials have been equivocal and sometimes contradictory. Finally, implications on the clinical management and public health recommendations concerning the use of nutritional supplements in chronic disease will be addressed. Likewise, issues around the marketing and advertising of these supplements in the general public will be also discussed.
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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 | 0.029 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.021 |
| Scholarly communication | 0.009 | 0.021 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.011 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".