Multiple micronutrient powders reduce stunting and anemia and improve language development among full‐term low birth weight children in Bangladesh (389.3)
Bibliographic record
Abstract
Objective: Explore efficacy of 22‐element micronutrient powders (MNP) plus nutrition, health and hygiene education (NHHE) to reduce stunting and anemia, and improve mental development among low birth weight (LBW) infants. Methods: Prospective community‐based cluster‐randomized trial, conducted among 244 full‐term LBW infants from 6‐12 months (mo) in rural Bangladesh. A total of 24 clusters were randomly assigned to two groups: i) NHHE plus MNP (to be provided with available complementary food); ii) NHHE only. Capillary blood was collected at 6 and 12mo using HemoCue. At ~18mo cognitive and language scores of children were assessed using Bayley Scales of Infant and Toddler Development III (n=186). Results: At 12 mo, compared to ‘NHHE only’, infants in ‘NHHE+MNP’ had significantly lower proportion of stunting (55.9% vs 33.6%, p<0.001) and anemia (23.9% vs 11.7%, p<0.001). Mean Hb concentration in the ‘NHHE+MNP’infants increased from 93.8±10.8 g/L at 6mo to 109.8±12.3 g/L at 12mo (p<0.05), whereas no such difference was found in ‘NHHE only’ group. Expressive language scores were significantly higher among ‘NHHE+MNP’ compared with ‘NHHE only’ (p<0.01; d=0.45). There were no differences between groups on cognitive or receptive language scores. Conclusion: Use of broad range micronutrient powders effectively reduce stunting and anemia and improve expressive language among term LBW children. Grant Funding Source : Bill & Melinda Gates Foundation to FHI 360, through the Alive & Thrive Small Grants Program managed
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".