Impact of provitamin A biofortified maize on vitamin A status in Zambian children (646.6)
Bibliographic record
Abstract
Vitamin A (VA) deficiency remains a nutritional concern in Sub‐Saharan Africa. We conducted a cluster‐randomized controlled trial to test the impact of proVA biofortified maize flour consumption on VA status. All 4‐8 y old children (n=1,226) in a study area of ~400 km² in Mkushi, Zambia were enrolled and grouped by proximity into clusters of ~15‐25. We randomized clusters to: a) biofortified “orange” maize flour (n=25 clusters); b) white maize flour (n=25); or 3) non‐intervened control (n=14). Intervened clusters received 200 g maize flour, 6 d/wk for 6 mo prepared as per standardized recipes. Food packages were given in non‐intervened clusters after the trial. At baseline and follow‐up, we collected venous blood to measure serum retinol, beta‐carotene, CRP, and AGP. Groups were comparable at baseline. Although attendance did not differ (85%), median daily intake was higher in white (156 g/d) vs orange (140 g/d; providing ~75% RDA for VA) clusters. At follow‐up, serum β‐carotene was 0.3 μmol/L (95% CI: 0.2‐0.4) higher in orange clusters (p<0.001), but mean serum retinol (1.06 ± 0.43 μmol/L overall) and VA deficiency prevalence (18.6% overall; <0.7 μmol/L) did not differ by trial arm. In this marginally nourished population, regular biofortified maize flour consumption did not improve VA status. Grant Funding Source : Supported by HarvestPlus and the Canadian International Development Agency
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".