A Micronutrient Fortified Beverage Given at Different Dosing Frequencies Had Limited Impact on Anemia and Micronutrient Status in Filipino Schoolchildren
Bibliographic record
Abstract
This study evaluated the effects of a multi-micronutrient fortified juice drink given in different frequencies of consumption on hemoglobin (Hb) concentration of schoolchildren. Hb was measured in 2423 schoolchildren aged 6- to 9-years-old at baseline. All anemic children (n = 246) were randomly allocated into groups: Daily dose (HD: high dose), 5X/week (MD: Moderate Dose), 3X/week (LD: Low Dose) and unfortified (Control). Pre- and post-study measurements of micronutrients were collected from 228 children. At the endpoint, significant Hb increases were observed in all groups, but there was no significant difference between groups. There was a significant reduction in anemia prevalence in all groups from 100% to 36% (Control), 30% (LD), 23% (MD) and 26% (HD). No dose-response effect was observed in Hb in this population. Most likely, this resulted from better than expected micronutrient status and lower than expected severity of anemia and micronutrient deficiencies in this cohort. It is unlikely that the addition of a fortified beverage to school feeding programs in this population would have a positive impact. Whether such an intervention would be cost-effective as a preventative approach needs to be assessed. This study demonstrates the importance of targeting such interventions to appropriate populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".