Iron status and inherited haemoglobin disorders modify the effects of micronutrient powders on linear growth and morbidity among young Lao children in a double-blind randomised trial
Bibliographic record
Abstract
Some studies found that providing micronutrient powder (MNP) causes adverse health outcomes, but modifying factors are unknown. We aimed to investigate whether Fe status and inherited Hb disorders (IHbD) modify the impact of MNP on growth and diarrhoea among young Lao children. In a double-blind controlled trial, 1704 children of age 6-23 months were randomised to daily MNP (with 6 mg Fe plus fourteen micronutrients) or placebo for about 36 weeks. IHbD, and baseline and final Hb, Fe status and anthropometrics were assessed. Caregivers provided weekly morbidity reports. At enrolment, 55·6 % were anaemic; only 39·3 % had no sign of clinically significant IHbD. MNP had no overall impact on growth and longitudinal diarrhoea prevalence. Baseline Hb modified the effect of MNP on length-for-age (LAZ) (P for interaction = 0·082). Among children who were initially non-anaemic, the final mean LAZ in the MNP group was slightly lower (-1·93 (95 % CI -1·88, -1·97)) v. placebo (-1·88 (95 % CI -1·83, -1·92)), and the opposite occurred among initially anaemic children (final mean LAZ -1·90 (95 % CI -1·86, -1·94) in MNP v. -1·92 (95 % CI -1·88, -1·96) in placebo). IHbD modified the effect on diarrhoea prevalence (P = 0·095). Among children with IHbD, the MNP group had higher diarrhoea prevalence (1·37 (95 % CI 1·17, 1·59) v. 1·21 (95 % CI 1·04, 1·41)), while it was lower among children without IHbD who received MNP (1·15 (95 % CI 0·95, 1·39) v. 1·37 (95 % CI 1·13, 1·64)). In conclusion, there was a small adverse effect of MNP on growth among non-anaemic children and on diarrhoea prevalence among children with IHbD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".