Magnitude and determinants of neural tube defect in Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Neural tube defects (NTDs) are a group of disorders that arise from the failure of the neural tube close between 21 and 28 days after conception. About 90% of neural tube defects and 95% of death due to these defects occurs in low-income countries. Since these NTDs cause considerable morbidity and mortality, this study aimed to determine the prevalence and associated factors of NTDs in Africa. Methods The protocol of this study was registered in the International Prospective Register of Systematic Reviews (PROSPERO number: CRD42020149356). All major databases such as PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, African Journals Online (AJOL), and Google Scholar search engine were systematically searched. A random-effect model was used to estimate the pooled prevalence of NTDs in Africa, and Cochran’s Q-statistics and I2 tests were used to assess heterogeneity between included studies. Publication bias was assessed using Begg ’s tests, and the association between determinant factors and NTDs was estimated using a random-effect model. Results Of the total 2679 articles, 37 articles fulfilled the inclusion criteria and were included in this systematic review and meta-analysis. The pooled prevalence of NTDs in Africa was 50.71 per 10,000 births (95% CI: 48.03, 53.44). Folic acid supplementation (AOR: 0.40; 95% CI: 0.19–0.85), maternal exposure to pesticide (AOR: 3.29; 95% CI: 1.04–10.39), mothers with a previous history of stillbirth (AOR: 3.35, 95% CI: 1.99–5.65) and maternal exposure to x-ray radiation (AOR 2.34; 95% CI: 1.27–4.31) were found to be determinants of NTDs. Conclusions The pooled prevalence of NTDs in Africa was found to be high. Maternal exposure to pesticides and x-ray radiation were significantly associated with NTDs. Folic acid supplementation before and within the first month of pregnancy was found to be a protective factor for NTDs.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".