Impact of Dietary Interventions during Pregnancy on Maternal, Neonatal, and Child Outcomes in Low- and Middle-Income Countries
Bibliographic record
Abstract
Optimal nutrition plays a crucial role in pregnancy. Maternal malnutrition is a risk factor for maternal, fetal, and neonatal complications and is more prevalent in low and middle-income countries (LMICs). This review aims to study the effectiveness of antenatal macronutrient nutritional interventions on maternal, neonatal, and child outcomes. We searched the CENTRAL, PubMed, Embase, and other databases for randomized controlled trials and quasi-experimental designs on healthy pregnant women in LMICs. We also searched grey literature and reports from Google Scholar, Web of Science, and websites of different organizations. Title/abstract screening, full-text screening, and data extraction filtered 15 studies for inclusion. Balanced energy protein (BEP) supplementation (n = 8) studies showed a reduced incidence of perinatal mortality, stillbirths, low birth weight (LBW) infants, small for gestational age (SGA) babies and increased birth weight. Food distribution programs (FDPs) (n =5) witnessed reduced rates of SGA, stunting, wasting, and increased birth weight and birth length. Studies on intervention for obesity prevention (n = 2) showed reductions in birth weight. Other findings were statistically insignificant. Subgroup analyses were conducted to study the effectiveness of supplementation between regions, location, the timing of supplementation and nutritional status; however, there were a limited number of studies in each subgroup. Data from our review supports the antenatal supplementation of BEP and FDP for the prevention of adverse maternal, neonatal, and child outcomes that can be utilized for future policymaking. However, more research is required before recommending obesity prevention programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".