Improving the iron status of school children through a school noon meal programme with meals prepared using a multiple micronutrient-fortified salt in Tamil Nadu, India.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: To improve the iron status of school children through noon meals prepared using a multiple micronutrient-fortified salt. METHODS AND STUDY DESIGN: Children from a randomly selected school who consumed (intervention) and did not consume (reference) a noon meal prepared using a multiple micronutrient- fortified salt were studied over 1 year. A pre-post-test design for children aged 5-17years in reference (n=100) and intervention (n=128) groups was used. Levels of serum ferritin, soluble transferrin receptor (sTfR), alpha glycoprotein (AGP), and C-reactive protein (CRP) were assessed at baseline and at 1 year. In a subsample, urinary iodine was assessed. RESULTS: sTfR decreased in the intervention group (-0.80 mg/L) but increased in the reference group (0.47 mg/L) at 1 year (p=0.0001).Body iron stores (BIS) increased in the intervention group (0.09 mg/kg body weight) and decreased (-0.58 mg/kg body weight) in the reference group at 1 year (p=0.028).These findings indicate an increase in iron deficiency in the reference group and a decrease in the intervention group. However, no changes in serum ferritin and urinary iodine were observed in either group or between groups. CONCLUSIONS: Iron status can be improved in schoolchildren in Tamil Nadu by increasing the amount of micronutrients in the fortified salt used for preparing noon-time school meals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".