Research on the severity of symptoms in children with ASD based on integrated machine learning and structural equation modeling: age-specific predictive features and mediation effect path analysis - Summary - MDSpire
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Research on the severity of symptoms in children with ASD based on integrated machine learning and structural equation modeling: age-specific predictive features and mediation effect path analysis
To investigate the relationship between age at first diagnosis and symptom severity in autism spectrum disorder (ASD), specifically exploring the mediating effects of developmental level and physical development on this relationship.
Key Findings:
Language was identified as the core predictive feature for the low-age group, indicating its critical role in early diagnosis.
In the high-age group, the importance of personal-social, fine motor, gross motor, and physical development increased significantly, while the importance of language declined, suggesting a shift in focus for interventions.
SEM indicated a masking effect of age at first diagnosis on symptom severity, with a positive indirect effect via developmental level and physical development, but a negative direct effect that offsets the total indirect effect.
Interpretation:
The study highlights age-specific predictive features and the complex relationship between age at first diagnosis, developmental level, and symptom severity in children with ASD, suggesting tailored interventions based on age.
Limitations:
The study may not account for all potential confounding variables affecting symptom severity, which could skew results.
The sample size and age range may limit the generalizability of the findings, necessitating further research with diverse populations.
Conclusion:
The findings provide preliminary insights for age-stratified interventions in children with ASD.
