{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T06:52:33Z","timestamp":1781679153976,"version":"3.54.5"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T00:00:00Z","timestamp":1781654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011833","name":"Guangxi University of Chinese Medicine","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011833","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pediatr."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The correlation between age at first diagnosis and symptom severity in autism spectrum disorder (ASD) remains inconsistent in existing research, and the age-specific mechanisms through which developmental level and physical development mediate this relationship remain to be elucidated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 608 children with ASD aged 24\u223c71 months at first diagnosis were recruited and stratified into a low-age group (24\u223c47 months) and a high-age group (48\u223c71 months). The Gesell Developmental Schedule (GDS) was utilized to assess developmental level, the Childhood Autism Rating Scale (CARS) to evaluate symptom severity, and height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and body mass index-for-age Z-score (BAZ) were calculated to assess physical development. Integrated machine learning was applied to construct classification models for distinguishing severe from non-severe ASD and to identify age-specific predictive features, and structural equation modeling (SEM) was used to analyze the mediation effects and path mechanisms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Machine learning revealed language as the core predictive feature for the low-age group; whereas in the high-age group, the predictive importance of personal-social, fine motor, gross motor, HAZ, and WAZ increased significantly, with a relative decline in the predictive importance of language. SEM showed a masking effect in the total sample: age at first diagnosis had a positive total indirect effect on symptom severity via developmental level and physical development, but a negative direct effect that offset the total indirect effect, resulting in a non-significant total effect. The mediating role of developmental level was significant, and the effect of developmental level on symptom severity was more pronounced in the low-age group.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study identifies age-specific predictive features\u2014language as the core predictive feature in the low-age group, whereas in the high-age group, the importance of personal-social, motor, and physical development increases while that of language declines. It also reveals a masking effect of age at first diagnosis on ASD symptom severity: a negative direct effect offset by a positive indirect effect via developmental level and physical development, resulting in a non-significant total effect. These findings provide a preliminary reference for age-stratified intervention in children with ASD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fped.2026.1841816","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T05:55:44Z","timestamp":1781675744000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.3389","volume":"14","author":[{"given":"Shimei","family":"Lu","sequence":"first","affiliation":[{"name":"Department of Preventive Medicine, Guangxi University of Chinese Medicine","place":["Nanning, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangqiong","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of 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