Complementary Feeding Diets Made of Local Foods Can Be Optimized, but Additional Interventions Will Be Needed to Meet Iron and Zinc Requirements in 6- to 23-Month-Old Children in Low- and Middle-Income Countries
Bibliographic record
Abstract
BACKGROUND: The question whether diets composed of local foods can meet recommended nutrient intakes in children aged 6 to 23 months living in low- and middle-income countries is contested. OBJECTIVE: To review evidence of studies evaluating whether (1) macro- and micronutrient requirements of children aged 6 to 23 months from low- and middle-income countries are met by the consumption of locally available foods ("observed intake") and (2) nutrient requirements can be met when the use of local foods is optimized, using modeling techniques ("modeled intake"). METHODS: Twenty-three articles were included after conducting a systematic literature search. To allow for comparisons between studies, findings of 15 observed intake studies were compared against their contribution to a standardized recommended nutrient intake from complementary foods. For studies with data on intake distribution, %< estimated average requirements were calculated. RESULTS: Data from the observed intake studies indicate that children aged 6 to 23 months meet requirements of protein, while diets are inadequate in calcium, iron, and zinc. Also for energy, vitamin A, thiamin, riboflavin, niacin, folate, and vitamin C, children did not always fulfill their requirements. Very few studies reported on vitamin B6, B12, and magnesium, and no conclusions can be drawn for these nutrients. When diets are optimized using modeling techniques, most of these nutrient requirements can be met, with the exception of iron and zinc and in some settings calcium, folate, and B vitamins. CONCLUSION: Our findings suggest that optimizing the use of local foods in diets of children aged 6 to 23 months can improve nutrient intakes; however, additional cost-effective strategies are needed to ensure adequate intakes of iron and zinc.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".