Effects of Combined Lipid-Based Nutrient Supplements and Malaria Preventive Treatment during Pregnancy on Malaria, Maternal Nutrition, and Birth Outcomes: A Scoping Review
Bibliographic record
Abstract
Malaria and malnutrition adversely affect maternal and neonatal health in Sub-Saharan Africa. While intermittent preventive treatment of malaria in pregnancy (IPTp) is widely used to reduce malaria incidence, the potential additional benefits of combining it with lipid-based nutrient supplements (LNS) remain unclear. The objective of this review was to systematically summarize the evidence on the effects of LNS in combination with IPTp on malaria incidence, maternal nutritional status, and birth outcomes. A comprehensive search of 4 databases-MEDLINE, EMBASE, Scopus, and CENTRAL-and the gray literature via GOOGLE Scholar-was conducted in January 2024, and updated in July 2024. The review followed PRISMA-ScR guidelines and included studies assessing LNS and IPTp for outcomes related to malaria, nutritional status, or birth outcomes. The review identified 17 studies, focused on 5 main trials: 2 with small-quantity LNS (SQ-LNS), 2 with balanced energy-protein (BEP), and 1 trial with large-quantity LNS (LQ-LNS). LNS supplementation did not significantly affect malaria incidence, anemia, or nutritional status. However, some studies reported improvements in birth outcomes, particularly among specific subgroups and those with baseline undernutrition. Variability in study methodologies and supplementation protocols influenced these findings. While LNS and IPTp show potential for improving selected birth outcomes, the evidence for their effect on malaria incidence or maternal anemia remains inconclusive. Further research is needed to assess the effectiveness of LNS and IPTp among vulnerable groups of women in malaria-endemic regions.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".