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Machine learning to predict poor school performance in paediatric survivors of intensive care: a population-based cohort study.
Gilholm P, Gibbons K, Brüningk S, Klatt J, Vaithianathan R, Long D, Millar J, Tomaszewski W, Schlapbach LJ; Australian and New Zealand Intensive Care Society (ANZICS) Centre for Outcomes & Resource Evaluation (CORE) and ANZICS Paediatric Study Group (ANZICS PSG). Gilholm P, et al. Intensive Care Med. 2023 Jul;49(7):785-795. doi: 10.1007/s00134-023-07137-1. Epub 2023 Jun 24. Intensive Care Med. 2023. PMID: 37354231 Free PMC article.
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We developed a machine learning model to predict poor school outcomes in children surviving intensive care unit (ICU). ...
PURPOSE: Whilst survival in paediatric critical care has improved, clinicians lack tools capable of predicting long-term outcomes. We …
Admissions of Children and Adolescents With Deliberate Self-harm to Intensive Care During the SARS-CoV-2 Outbreak in Australia.
Corrigan C, Duke G, Millar J, Paul E, Butt W, Gordon M, Coleman J, Pilcher D, Oberender F; Australian and New Zealand Intensive Care Society Pediatric Study Group (ANZICS PSG) and the ANZICS Center for Outcome and Resource Evaluation (ANZICS CORE). Corrigan C, et al. JAMA Netw Open. 2022 May 2;5(5):e2211692. doi: 10.1001/jamanetworkopen.2022.11692. JAMA Netw Open. 2022. PMID: 35544133 Free PMC article.
This occurred while the rate of all-cause admissions to pediatric ICUs of children and adolescents of all ages (ie, ages 0-17 years) per 1 million children and adolescents decreased from a long-term monthly median (IQR) of 150.9 (138.1-159.8) admissions to 91.7 admissions …
This occurred while the rate of all-cause admissions to pediatric ICUs of children and adolescents of all ages (ie, ages 0-17 years) per 1 m …