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      Community Psychosis Risk Screening: An Instrument Development Investigation

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          Abstract

          Schizophrenia and other psychotic disorders are serious psychiatric disorders that are associated with substantial societal, family, and individual costs/distress. Evidence suggests that early intervention can improve prognostic outcomes; therefore, it is essential to accurately identify those at risk for psychosis before full psychotic symptoms emerge. The purpose of our study is to develop a brief, valid screening questionnaire to identify individuals at risk for psychosis in non-clinical populations across 3 large, community catchment areas with diverse populations. This is a needed study, as the current screening tools for at-risk psychotic populations in the US have been validated only in clinical and/or treatment seeking samples, which are not likely to generalize beyond these specialized settings. The specific aims are as follows: (1) to determine norms and prevalence rates of attenuated positive psychotic symptoms across 3 diverse, community catchment areas and (2) to develop a screening questionnaire, inclusive of both symptom-based and risk factor-based questions. Our study will develop an essential screening tool that will identify which individuals have the greatest need of follow-up with structured interviews in both research and clinical settings. Our study has the potential for major contributions to the early detection and prevention of psychotic disorders.

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          Most cited references 111

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          Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine.

          The clinical performance of a laboratory test can be described in terms of diagnostic accuracy, or the ability to correctly classify subjects into clinically relevant subgroups. Diagnostic accuracy refers to the quality of the information provided by the classification device and should be distinguished from the usefulness, or actual practical value, of the information. Receiver-operating characteristic (ROC) plots provide a pure index of accuracy by demonstrating the limits of a test's ability to discriminate between alternative states of health over the complete spectrum of operating conditions. Furthermore, ROC plots occupy a central or unifying position in the process of assessing and using diagnostic tools. Once the plot is generated, a user can readily go on to many other activities such as performing quantitative ROC analysis and comparisons of tests, using likelihood ratio to revise the probability of disease in individual subjects, selecting decision thresholds, using logistic-regression analysis, using discriminant-function analysis, or incorporating the tool into a clinical strategy by using decision analysis.
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            The development of a Clinician-Administered PTSD Scale.

            Several interviews are available for assessing PTSD. These interviews vary in merit when compared on stringent psychometric and utility standards. Of all the interviews, the Clinician-Administered PTSD Scale (CAPS-1) appears to satisfy these standards most uniformly. The CAPS-1 is a structured interview for assessing core and associated symptoms of PTSD. It assesses the frequency and intensity of each symptom using standard prompt questions and explicit, behaviorally-anchored rating scales. The CAPS-1 yields both continuous and dichotomous scores for current and lifetime PTSD symptoms. Intended for use by experienced clinicians, it also can be administered by appropriately trained paraprofessionals. Data from a large scale psychometric study of the CAPS-1 have provided impressive evidence of its reliability and validity as a PTSD interview.
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              Internal validation of predictive models: efficiency of some procedures for logistic regression analysis.

              The performance of a predictive model is overestimated when simply determined on the sample of subjects that was used to construct the model. Several internal validation methods are available that aim to provide a more accurate estimate of model performance in new subjects. We evaluated several variants of split-sample, cross-validation and bootstrapping methods with a logistic regression model that included eight predictors for 30-day mortality after an acute myocardial infarction. Random samples with a size between n = 572 and n = 9165 were drawn from a large data set (GUSTO-I; n = 40,830; 2851 deaths) to reflect modeling in data sets with between 5 and 80 events per variable. Independent performance was determined on the remaining subjects. Performance measures included discriminative ability, calibration and overall accuracy. We found that split-sample analyses gave overly pessimistic estimates of performance, with large variability. Cross-validation on 10% of the sample had low bias and low variability, but was not suitable for all performance measures. Internal validity could best be estimated with bootstrapping, which provided stable estimates with low bias. We conclude that split-sample validation is inefficient, and recommend bootstrapping for estimation of internal validity of a predictive logistic regression model.
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                Author and article information

                Journal
                101739159
                48282
                J Psychiatr Brain Sci
                J Psychiatr Brain Sci
                Journal of psychiatry and brain science
                2398-385X
                5 September 2020
                20 August 2020
                2020
                16 September 2020
                : 5
                Affiliations
                [1 ]Department of Psychology, Temple University, Philadelphia, 19122, PA, USA
                [2 ]Department of Psychology, University of Maryland-Baltimore County, Baltimore, 21228, MD, USA
                [3 ]Department of Psychological Science, University of California, Irvine, Irvine, 92697, CA, USA
                [4 ]Departments of Psychology and Psychiatry and Behavioral Sciences, Northwestern University, Evanston, 60208, IL, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                LME conceived of the MAP study, is the coordinating PI on the funded grant, and wrote the manuscript. VAM and JS contributed to the development of the study protocol and substantially contributed to the development of the grant application over the course of 4 years of iterations of the application. VAM and JS also read this manuscript and contributed edits and ideas to the final submission.

                [* ]Correspondence: Lauren M. Ellman, ellman@ 123456temple.edu .
                Article
                NIHMS1624005
                10.20900/jpbs.20200019
                7494215
                32944657

                This is an open access article distributed under the terms and conditions of Creative Commons Attribution 4.0 International License.

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