Large-scale food fortification has great potential to improve child health and nutrition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Undernutrition, including micronutrient deficiencies, continues to plague children across the world, particularly in low and middle-income countries (LMICs). The situation has worsened alongside the SARS-CoV-2 pandemic because of major systemic disruptions to food supply, healthcare, and employment. Large-scale food fortification (LSFF) is a potential strategy for improving micronutrient intakes through the addition of vitamins and minerals to staple foods and improving the nutritional status of populations at large. RECENT FINDINGS: Current evidence unquestionably supports the use of LSFF to improve micronutrient status. Evidence syntheses have also demonstrated impact on some functional outcomes, including anemia, wasting, underweight, and neural tube defects, that underpin poor health and development. Importantly, many of these effects have also been reflected in effectiveness studies that examine LSFF in real-world situations as opposed to under-controlled environments. However, programmatic challenges must be addressed in LMICs in order for LSFF efforts to reach their full potential. SUMMARY: LSFF is an important strategy that has the potential to improve the health and nutrition of entire populations of vulnerable children. Now more than ever, existing programs should be strengthened and new programs implemented in areas with widespread undernutrition and micronutrient deficiencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".