Vitamin A supplementation and neonatal mortality in the developing world: a meta-regression of cluster-randomized trials
Bibliographic record
Abstract
OBJECTIVE: To assess the relationship between the prevalence of vitamin A deficiency among pregnant women and the effect of neonatal vitamin A supplementation on infant mortality. METHODS: Studies of neonatal supplementation with vitamin A have yielded contradictory findings with regard to its effect on the risk of infant death, possibly owing to heterogeneity between studies. One source of that heterogeneity is the prevalence of vitamin A deficiency among pregnant women, which we examined using meta-regression techniques on eligible individual and cluster-randomized trials. Adapting standard techniques to control for the inclusion of a cluster-randomized trial, we modelled the logarithm of the relative risk of infant death comparing vitamin A supplementation at birth to a standard treatment, as a linear function of the prevalence of vitamin A deficiency in pregnant women. FINDINGS: Meta-regression analysis revealed a statistically significant linear relationship between the prevalence of vitamin A deficiency in pregnant women and the observed effectiveness of vitamin A supplementation at birth. In regions where at least 22% of pregnant women have vitamin A deficiency, giving neonates vitamin A supplements will have a protective effect against infant death. CONCLUSION: A meta-regression analysis is observational in nature and may suffer from confounding bias. Nevertheless, our study suggests that vitamin A supplementation can reduce infant mortality in regions where this micronutrient deficiency is common. Thus, neonatal supplementation programmes may prove most beneficial in regions where the prevalence of vitamin A deficiency among pregnant women is high.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".