Does oral iron and folate supplementation during pregnancy protect against adverse birth outcomes and reduced neonatal and infant mortality in Africa: A protocol for a systematic review and meta-analysis?
Bibliographic record
Abstract
Background: Globally, one-third of pregnant women are at risk of iron deficiency, particularly in the African region. While recent findings show that iron and folate supplementation can lower the risk of adverse birth outcomes and childhood mortality, our understanding of its impact in Africa remains incomplete due to insufficient evidence. This protocol outlines the systematic review steps to investigate the impact of oral iron and folate supplementation during pregnancy on adverse birth outcomes, neonatal mortality and infant mortality in Africa. Methods and analysis: MEDLINE, PsycINFO, Embase, Scopus, CINAHL, Web of Science, and Cochrane databases were searched for published articles. Google Scholar and Advanced Google Search were used for gray literature and nonindexed articles. Oral iron and/or folate supplementation during pregnancy is the primary exposure. The review will focus on adverse birth outcomes, neonatal mortality and infant mortality. Both Cochrane Effective Practice and Organization of Care and Newcastle-Ottawa Scale risk of bias assessment tools will be used. Meta-analysis will be conducted if design and data analysis methodologies permit. This systematic review and meta-analysis will provide up-to-date evidence about iron and folate supplementation's role in adverse birth outcomes, neonatal mortality and infant mortality in the African region. Ethics and dissemination: This review will provide insights that help policymakers, program planners, researchers, and public health practitioners interested in working in the region. PROSPERO registration number: CRD42023452588.
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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 | 0.103 | 0.154 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".