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This initial construction study suggests that the PDS-ICD-11 constitutes a promising instrument that provides a quick impression of the severity of personality dysfunction according to the official ICD-11 PD guidelines. Clearly, more research is needed to corroborate its validity and utility. The PDS-ICD-11 scale is provided as online supporting information.
This initial construction study suggests that the PDS-ICD-11 constitutes a promising instrument that provides a quick impression of the severity of personality dysfunction according to the official ICD-11 PD guidelines. Clearly, more research is needed to corroborate its validity and utility. The PDS-ICD-11 scale is provided as online supporting information.Previous studies have documented the utility of a transdiagnostic internalizing factor in predicting important future outcomes (e.g., subsequent mental disorder diagnoses). To date, however, no study has investigated whether an internalizing factor predicts mortality risk. Also, while pre-vious studies of mortality risk have emphasized its associations with particular internalizing disorders, no study has assessed how the transdiagnostic internalizing factor vs. disorder-specific variance differently predict that risk. The primary aims of this study were to explore a) whether the internalizing factor predicts mortality risk, b) whether particular internalizing psychopathologies uniquely predict mortality risk over and beyond the transdiagnostic internalizing factor, and c) whether there is a significant interaction of internalizing with self-reported health in the prediction of mortality risk. We utilized a large national sample of American adults from the Midlife in the United States (MIDUS), a longitudinal ividuals with excellent self-reported health condition (HR=1.50, 95% CI 1.17-1.84, p less then 0.05). This highlights the clinical utility of using the transdiagnostic internalizing factor for prediction of an important future outcome, and supports the argument that internalizing psychopathology can be a meaningful liability to explore in public health practice.This study aimed to determine whether, following two years of specialized support for first-episode psychosis, the addition of a new digital intervention (Horyzons) to treatment as usual (TAU) for 18 months was more effective than 18 months of TAU alone. We conducted a single-blind randomized controlled trial. Participants were people with first-episode psychosis (N=170), aged 16-27 years, in clinical remission and nearing discharge from a specialized service. selleck kinase inhibitor They were randomly assigned (11) to receive Horyzons plus TAU (N=86) or TAU alone (N=84) between October 2013 and January 2017. Horyzons is a novel, comprehensive digital platform merging peer-to-peer social networking; theory-driven and evidence-informed therapeutic interventions targeting social functioning, vocational recovery and relapse prevention; expert clinician and vocational support; and peer support and moderation. TAU involved transfer to primary or tertiary community mental health services. The primary outcome was social functioning at 18 m So, although we did not find a significant effect of Horyzons on social functioning compared with TAU, the intervention was effective in improving vocational or educational attainment, a core component of social recovery, and in reducing usage of hospital emergency services, a key aim of specialized first-episode psychosis services. Horyzons holds significant promise as an engaging and sustainable intervention to provide effective vocational and relapse prevention support for young people with first-episode psychosis beyond specialist services.The Hierarchical Taxonomy of Psychopathology (HiTOP) is an empirical effort to address limitations of traditional mental disorder diagnoses. These include arbitrary boundaries between disorder and normality, disorder co-occurrence in the modal case, heterogeneity of presentation within dis-orders, and instability of diagnosis within patients. This paper reviews the evidence on the validity and utility of the disinhibited externalizing and antagonistic externalizing spectra of HiTOP, which together constitute a broad externalizing superspectrum. These spectra are composed of elements subsumed within a variety of mental disorders described in recent DSM nosologies, including most notably substance use disorders and "Cluster B" personality disorders. The externalizing superspectrum ranges from normative levels of impulse control and self-assertion, to maladaptive disinhibition and antagonism, to extensive polysubstance involvement and personality psychopathology. A rich literature supports the validity of the externalizing superspectrum, and the disinhibited and antagonistic spectra. This evidence encompasses common genetic influences, environmental risk factors, childhood antecedents, cognitive abnormalities, neural alterations, and treatment response. The structure of these validators mirrors the structure of the phenotypic externalizing superspectrum, with some correlates more specific to disinhibited or antagonistic spectra, and others relevant to the entire externalizing superspectrum, underlining the hierarchical structure of the domain. Compared with traditional diagnostic categories, the externalizing superspectrum conceptualization shows improved utility, reliability, explanatory capacity, and clinical applicability. The externalizing superspectrum is one aspect of the general approach to psychopathology offered by HiTOP and can make diagnostic classification more useful in both research and the clinic.For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing.
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