Risk prediction
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Are ACE scores useful for identifying individuals at risk of health problems?
Clinics are increasingly screening for ACEs, but ACE scores may not tell us who will go on to develop poor health, explain Jessie R Baldwin (pic) and Andrea Danese.
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How can we model the brain when it goes awry? How Reinforcement Learning Models can shed light on Psychiatric Disorders that emerge during Development.
It is well-established that many psychiatric disorders initially emerge during the formative time periods of childhood and adolescence (Kessler et al., 2005; Paus, Keshavan, & Giedd, 2008), when the brain is consistently subject to growth and experience-related changes. This applies not only to classic neurodevelopmental disorders like attention deficit hyperactivity disorder (ADHD) but also to psychiatric disorders like depression or obsessive-compulsive disorder (OCD), which are often attributed to adulthood (Hauser, Will, Dubois, & Dolan, 2019).
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Making personalised predictions of poor functioning following negative childhood experiences
Experiencing abuse, neglect, bullying, or domestic violence in childhood increases the likelihood of having poor functioning in young adulthood, but this is not the case for everyone. Being able to accurately predict which individuals are at high risk for poor outcomes following such negative childhood experiences could support professionals to effectively target interventions. Is it possible to make accurate personalised predictions?
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November 2020 – The Bridge
The research featured in this issue covers a wide range of topics relevant to our work with young people, including neurodevelopmental, emotional, and behavioural disorders, their comorbidity, and their links with functioning and quality of life.
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Results of the ACAMH Awards 2020
Congratulations to all winners and nominees of the ACAMH Awards 2020.
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Genetic and environmental influences on callous-unemotional traits vary with age
Research on callous-unemotional (CU) traits explores the relative importance of genetic versus environmental influences on the initial risk and trajectory.
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Can population registry data predict which children with ADHD are at risk of later substance use disorders?
The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.
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Can we predict (complex) PTSD in young people in foster care?
Adverse, early life experiences put young people at risk of developing psychological difficulties. Potential difficulties might include post-traumatic stress disorder (PTSD) or the newly proposed, complex PTSD.
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March 2020 – The Bridge
This edition of The Bridge features research digests on ‘FRIENDS’ and anxiety, CAMHS and technology training, OCD and anxiety, parenting, autism and more.
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