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    Predicting the onset of psychosis in patients at clinical high risk: practical guide to probabilistic prognostic reasoning

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    Prediction of psychosis in patients at clinical high risk (CHR) has become a mainstream focus of clinical and research interest worldwide. When using CHR instruments for clinical purposes, the predicted outcome is but only a probability; and, consequently, any therapeutic action following the assessment is based on probabilistic prognostic reasoning. Yet, probabilistic reasoning makes considerable demands on the clinicians. We provide here a scholarly practical guide summarising the key concepts to support clinicians with probabilistic prognostic reasoning in the CHR state. We review risk or cumulative incidence of psychosis in, person-time rate of psychosis, Kaplan-Meier estimates of psychosis risk, measures of prognostic accuracy, sensitivity and specificity in receiver operator characteristic curves, positive and negative predictive values, Bayes’ theorem, likelihood ratios, potentials and limits of real-life applications of prognostic probabilistic reasoning in the CHR state. Understanding basic measures used for prognostic probabilistic reasoning is a prerequisite for successfully implementing the early detection and prevention of psychosis in clinical practice. Future refinement of these measures for CHR patients may actually influence risk management, especially as regards initiating or withholding treatment

    Developmental aspects in clinical high risk states of psychosis

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    The early detection of psychotic disorders mainly utilizes two symptomatic clinical high risk (CHR) approaches: the ultra-high risk (UHR) and the basic symptom (BS) criteria. Initially involving mainly adult samples, CHR research increasingly moved into younger age groups. Yet concerns about the likely impact of age, i.e., developmental aspects, on the prevalence, clinical significance an psychosis-predictive
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