Perspective: Putting the youngest among us into the nutrition “call for action” for food fortification strategies
Bibliographic record
Abstract
Adequate iron intake is essential for optimal child development, but iron deficiency and anemia among infants and young children are widespread in low- and middle-income countries. Large-scale food fortification strategies hold great promise for reducing micronutrient deficiencies; however, for children <2 y of age, the impact of such strategies is limited because their intake of staple foods is relatively low and fortification levels are targeted at the adult population. Iron supplementation, iron fortification of foods targeted to infants, and point-of-use fortification with iron-containing products such as multiple micronutrient powders (MNPs) and small-quantity lipid-based nutrient supplements are evidence-based approaches recommended to reduce anemia among infants and young children when used in the right context. Since 2003, the WHO, with support from UNICEF, has recommended the use of MNPs to control iron deficiency. However, the percentage of children with anemia has changed very little over the past 10 y. Five years ago the UN declared a decade of action on nutrition, including World Health Assembly (WHA) targets for maternal, infant, and young child nutrition, yet the WHA set no anemia targets for children. In July 2020 the leaders of 4 UN agencies issued a call for action to protect children's right to nutrition in the face of the COVID-19 pandemic and beyond. Given persistently high rates of anemia among young children, the negative developmental impact, the challenge of meeting iron needs from typical complementary food diets, and the availability of successful evidence-based fortification strategies for this age group, we encourage planners, speakers, and donors at this year's UN Food Systems Summit and the Tokyo Nutrition for Growth Summit to 1) call for the WHA to set anemia targets for infants and young children and 2) promote investment in evidence-based interventions to improve the iron status of young children.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.039 | 0.049 |
| Insufficient payload (model declined to judge) | 0.019 | 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".