Micronutrient supplementation: when is best and why?
Bibliographic record
Abstract
For many nutrients, a systematic determination of the effects of high intakes over extended periods of time has not been conducted. Governments and scientific bodies have just begun to establish the methodology for, and to conduct, nutrient risk assessments for setting 'tolerable upper levels of intake' (UL) for nutrients. Nutrient risk assessment provides the framework for using available information to evaluate the safety of nutrients when added to foods or when consumed as supplements, in order to minimize the risks from over-consumption. When intakes are inadequate, food fortification may be the appropriate choice for some nutrients, while in other situations, when requirements are markedly higher for some population subgroups than for the general population, supplements may be the most appropriate intervention. The present paper will present some examples of how to use the UL along with food consumption data to assess the appropriateness of food fortification v. supplementation strategies and to assess their impact on nutrient intakes of the population. The important steps to be followed when evaluating which approach is best are: (a) establishing need, i.e. assessing the gap between current and desired intakes; (b) assessing safety, i.e. consider the margin of safety between requirement and UL as well as the severity and reversibility of the adverse effect that was used to establish the UL; (c) estimating exposure through statistical modelling, in which population-based estimates of intakes before and after the intervention are compared; (d) monitoring the impact of the intervention to ensure that the desired benefits are achieved and that excessive intakes are minimized. This approach can optimize the public health benefits of food fortification or supplement use while minimizing the risks due to excessive intakes.
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 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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".