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- W4289712071 abstract "Abstract A major goal of precision medicine is to predict prognosis based on individualized information at the earliest possible points in development. Using early snapshots of adaptive functioning and unsupervised data-driven discovery methods, we uncover highly stable early autism subtypes that yield information relevant to later prognosis. Data from the National Institute of Mental Health Data Archive (NDA) (n=1,098) was used to uncover 3 early subtypes (<72 months) that generalize with 97% accuracy. Outcome data from NDA (n=2,561; mean age, 13 years) also reproducibly clusters into 3 subtypes with 99% generalization accuracy. Early snapshot subtypes predict developmental trajectories in non-verbal cognitive, language, and motor domains and are predictive of membership in different adaptive functioning outcome subtypes. Robust and prognosis-relevant subtyping of autism based on early snapshots of adaptive functioning may aid future clinical and research work (e.g., clinical trials, intervention), via prediction of these subtypes with our open web-based app ( https://landiit.shinyapps.io/vineland_statification_proj/ )." @default.
- W4289712071 created "2022-08-04" @default.
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- W4289712071 date "2022-08-02" @default.
- W4289712071 modified "2023-09-26" @default.
- W4289712071 title "Prognostic early snapshot stratification of autism based on adaptive functioning" @default.
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- W4289712071 doi "https://doi.org/10.1101/2022.08.01.22278267" @default.
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