National strategies for screening neural tube defects in Saudi Arabia: activating prevention and early intervention
Bibliographic record
Abstract
Background: Neural tube defects (NTDs) are serious congenital anomalies affecting the brain and spinal cord. Despite widespread folic acid supplementation and food fortification programs, regions such as Saudi Arabia have not experienced a proportional decline in NTD prevalence. This narrative review evaluates the multifactorial contributors to NTDs, focusing on the effectiveness of current prevention and screening strategies both globally and within Saudi Arabia. Materials and methods: A comparative methodology guided this review, drawing from studies published between 2000 and 2024 sourced from PubMed, Scopus, and the WHO library. Keywords included "neural tube defects," "folic acid supplementation," "screening programs," and "food fortification." While not a systematic review, PRISMA principles were loosely followed to ensure study relevance and rigor. Results: Globally, countries like the United States, Canada, Chile, and Australia have implemented mandatory folic acid fortification and reported NTD reductions ranging from 19 to 78%. South Africa, for example, achieved a 66% decline in NTD-related deaths post-fortification. In Saudi Arabia, similar initiatives have been launched, including folic acid campaigns and food fortification. However, national-level data evaluating their impact remains sparse. Regional disparities in implementation, awareness, and access have limited the success of these measures. Although 80.1% of Saudi women reportedly understand the preventive role of folic acid, uptake and proper timing of supplementation remain inconsistent. Screening services, particularly in rural areas, are not uniformly accessible, reducing early detection rates. Unlike countries such as Australia and Chile, Saudi Arabia lacks a standardized system for tracking and evaluating NTD outcomes. Conclusion: This review concludes that while Saudi Arabia has adopted commendable preventive strategies, the absence of comprehensive data, policy enforcement, and public education limits their effectiveness. Strengthening national monitoring systems, ensuring equitable access to screening, and enforcing mandatory fortification policies modelled on successful international practices are critical. Adopting evidence-based policies supported by robust evaluation frameworks will be essential to reducing the burden of NTDs and improving maternal and child health outcomes in Saudi Arabia.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".