A systematic review of the correlation between exposure to environmental pollution and autism in children under 9 years in Middle Eastern countries
Bibliographic record
Abstract
Abstract Background Over the past decade, there has been a significant increase in the prevalence of autism spectrum disorder (ASD) worldwide. However, the precise causes of this disorder remain unclear. This review seeks to explore the potential link between environmental pollution and the development of autism spectrum disorder in children aged 9 and under in the Middle East. Method The research was conducted by searching across three electronic databases: PubMed, Scopus, and Web of Science databases using a combination of related terms. The inclusion criteria were all quantitative studies published in peer-reviewed journals in the English language between 2000 and 2023. Each study’s quality was evaluated using a modified version of the Newcastle–Ottawa Scale for cross-sectional studies. Narrative synthesis was used for data analysis. Results Out of 78 records retrieved, 7 studies met the inclusion criteria. The results indicate that exposure to environmental pollutants during childhood growth and development may have significant associations with ASD. However, there is a dearth of quality evidence on this subject, with few studies conducted in the Middle East, and those that exist often lack rigor. Conclusion Research highlights the significance of preventing environmental degradation and reducing pollutant emissions in the Middle East to mitigate the effects on child mental health. Further research on the relationship between environmental toxins and ASD is deemed essential for public health and societal welfare.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".