{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:31:10Z","timestamp":1786516270690,"version":"build-2736575974"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Behaviors of concern, such as aggression and self-injury, are common in children with neurodevelopmental disorders and contribute substantially to caregiver stress and health system burden. However, most evidence on behaviors is drawn from diagnostically homogeneous samples, limiting relevance to real-world presentations encountered in tertiary care. Our aim was to characterize the diagnostic and behavioral complexity of children referred to tertiary clinics and use machine learning to identify transdiagnostic features associated with behaviors across diverse neurodevelopmental presentations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>This retrospective chart review and analysis examined all children aged 2 to 17 years undergoing first-time assessment at a tertiary developmental pediatrics clinic between May 2022-2023. Unstructured physician notes and supporting documentation were transformed into structured data and supervised machine learning models identified factors associated with behaviors of concern.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Among 600 children, 83% exhibited at least one behavior of concern (mean, 3.15 [SD 2.98]). Most had multiple neurodevelopmental diagnoses (73%) and frequent co-occurrence of physical (80%) and mental health conditions (21%). Emotional dysregulation and non-cooperation were the most common behaviors, while aggression and self-injury were frequently moderate to severe. Machine learning models identified sleep difficulties, speech and language delay, restricted and repetitive behaviors (B symptoms) of autism, gastrointestinal issues, and service use as top-ranked associated factors across multiple behavioral types. Factors associated with any behaviors included ADHD, sensory impairments, and genetic contributors to autism.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Transdiagnostic factors highlight key domains often under-assessed in routine care. These findings support integrated assessment models that address modifiable clinical correlates for behaviors of concern across diagnostic boundaries to improve outcomes for high-need neurodevelopmental populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1739597","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:57:06Z","timestamp":1786514226000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Transdiagnostic clinical correlates for behaviors of concern in pediatric neurodevelopmental disorders: a retrospective chart review and machine learning analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Myka","family":"Estes","sequence":"first","affiliation":[{"name":"Department of Pediatrics, Cumming School of Medicine, University of Calgary","place":["Calgary, AB, 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