Excess micronutrient intake: defining toxic effects and upper limits in vulnerable populations
Bibliographic record
Abstract
Excessive micronutrient intake causes a variety of adverse health effects, depending on dose and duration. The risk of excess intake carries significant implications for micronutrient delivery interventions, particularly when such programs are overlapping. To minimize risk and provide public health guidance, several countries and the Food and Agriculture Organization of the United Nations/World Health Organization have set upper intake levels (ULs) for various life-stage populations using the risk assessment framework. However, there is a lack of international consensus on the actual ULs due to variability in application of this framework and a scarcity of evidence from which to draw upon, especially for children. Often ULs for children are established through a downward weight-based extrapolation from adult ULs, which is not always appropriate. The published ULs of nine organizations are compared, recent population nutrient intake evidence is presented, and the toxic effects of key minerals and vitamins are reviewed. Finally, the evidence for toxicity and setting of ULs for each nutrient is discussed including a comment on our degree of confidence in the strength of existing individual ULs. Challenges with risk assessment and opportunities for strengthening the definition of ULs are 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 | 0.033 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".