Randomized comparison of 3 types of micronutrient supplements in Ghanaian infants
Bibliographic record
Abstract
Assuring adequate micronutrient status of infants is a challenge. We compared the efficacy and acceptability of “Sprinkles” (SP), “Nutritabs” (NT, a crushable tablet) and “Nutributter” (NB, a peanut based spread), when added to home‐prepared complementary foods in Ghana. All products contained iron (SP 12.5 mg Fe as ferrous fumarate; NT & NB 9 mg Fe as ferrous sulfate), zinc, vitamins A and C and folic acid but differed in the content of energy and other nutrients. Infants ( n = 313) were randomly assigned to receive SP, NT or NB daily from 6 to 12 mo of age. At 6 and 12 mo, infants were measured and blood samples were collected. Non‐Intervention (NI) infants, who were eligible but not randomly selected for the intervention (n=96), were assessed at 12 mo only. All of the supplements were well accepted, and compliancewas similar among intervention groups. The 3 intervention groups did not differ in iron status or hemoglobin at 12 mo, but all 3 had significantly higher ferritin and lower transferrin receptor than the NI group. Mean (± SD) hemoglobin (g/L) was significantly higher in NT (112 ± 14) and NB (114 ± 14) but not in SP (110 ± 14) infants, compared to NI infants (106 ± 14). Results for other indices of micronutrient status are pending. Controlling for initial size, at 12 mo the NB group had significantly higher (effect size ~0.3) weight‐for‐age and length‐for‐age than the SP and NT groups, which were similar to the NI group. We conclude that all 3 supplements were well accepted and had a similar effect on iron status, but only NB increased infant growth. Supported by Nestle Foundation and USAID.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".