{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T10:32:44Z","timestamp":1787567564445,"version":"build-2736575974"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J intellect Disabil Res"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Global developmental delay (GDD) frequently precedes intellectual disability (ID), but no validated multivariable prognostic tool exists to support individualised counselling during the initial diagnostic work\u2010up. Existing risk indicators are typically considered in isolation, and their joint contribution within an interpretable predictive framework remains uncertain.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We retrospectively analysed 2453 children diagnosed with GDD between January 2014 and December 2023 at a provincial tertiary children's rehabilitation centre, all followed to a minimum age of 60\u2009months. Twenty\u2010eight candidate predictors covering perinatal, developmental, neuroimaging, electrophysiological, genetic and comorbidity domains were retained after multiple imputation, multicollinearity screening with random\u2010forest importance protection and standardisation. Five algorithms\u2014L2\u2010 and L1\u2010regularised logistic regression, random forest, XGBoost and LightGBM\u2014were trained on a stratified 70% training partition with class\u2010weight rebalancing; no synthetic minority over\u2010sampling was applied. Probabilities from the L2 model were post hoc recalibrated by Platt scaling. We evaluated discrimination, calibration (slope and intercept after Platt scaling), Brier score and net benefit on the held\u2010out 30% test set, with 1000 bootstrap confidence intervals. Sensitivity analyses excluded post\u2010baseline candidate predictors, and we benchmarked the full model against parsimonious one\u2010, three\u2010 and five\u2010feature regressions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of the cohort, 2037 children (83.0%) progressed to ID, reflecting the referral profile of a tertiary centre. The Platt\u2010calibrated L2 logistic regression achieved an AUC of 0.783 (95% CI 0.735\u20130.828) with calibration slope 1.01 and intercept \u22120.005, and a Brier score of 0.113 (95% CI 0.097\u20130.130). All five algorithms performed within a 0.015 AUC band. The strongest independent risk factors were pathogenic genetic variant pathogenicity (OR 2.13, 95% CI 1.86\u20132.44), comorbid autism spectrum disorder (OR 1.69, 95% CI 1.41\u20132.03) and EEG epileptiform discharges (OR 1.43, 95% CI 1.18\u20131.74); higher Gesell adaptive developmental quotient was the strongest protective factor (OR 0.45 per standardised unit, 95% CI 0.34\u20130.60). At the Youden\u2010optimal threshold of 0.85, sensitivity, specificity, positive and negative predictive values were 66.1%, 76.8%, 93.3% and 31.7%, respectively. Removal of early intensive intervention from the model lowered AUC by only 0.012 (95% CI\u2009\u2212\u20090.002 to 0.026), indicating that retrospective treatment information was not the primary driver of model performance. A parsimonious five\u2010feature model achieved AUC 0.762, recovering most of the discriminative signal. Discrimination was robust to strict exclusion of post\u2010baseline predictors (AUC 0.769), to complete\u2010case analysis (0.839) and to restriction to genetically tested children (0.864).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Among children referred to a tertiary centre with GDD, an interpretable, well\u2010calibrated logistic regression model integrating routinely available clinical, neurophysiological and genetic data stratifies the risk of progression to ID with moderate discrimination and high positive predictive value. The model is suitable for high\u2010risk triage; its application to community populations will require refitting and external validation. The accompanying nomogram and SHAP explanations support clinician\u2010facing risk communication.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jir.70164","type":"journal-article","created":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T09:24:57Z","timestamp":1787563497000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pathogenic Genetic Variants, Comorbid Autism and Adaptive Developmental Quotient as Independent Predictors of Intellectual Disability in Children With Global Developmental Delay: An Interpretable Machine Learning Model With Calibrated Risk Estimation"],"prefix":"10.1111","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3025-3573","authenticated-orcid":false,"given":"Wang","family":"Yiwen","sequence":"first","affiliation":[{"name":"Children's Rehabilitation Department Third Affiliated Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Junying","sequence":"additional","affiliation":[{"name":"Children's Rehabilitation Department Third Affiliated Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9595-8593","authenticated-orcid":false,"given":"Zhu","family":"Dengna","sequence":"additional","affiliation":[{"name":"Children's Rehabilitation Department Third Affiliated Hospital of Zhengzhou University  Zhengzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"crossref","DOI":"10.1176\/appi.books.9780890425596","volume-title":"Diagnostic and Statistical Manual of Mental Disorders","author":"American Psychiatric Association","year":"2013"},{"key":"e_1_2_13_3_1","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big Data and Machine Learning in Health Care","volume":"319","author":"Beam A. 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