Differentiating Bipolar Disorder from Major Depressive Disorder Using the MMPI-2-RF: A Receiver Operating Characteristics (ROC) Analysis
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This study investigated the ability of the MMPI Wiener–Harmon subtle subscales (on scales D, Hy, Pd, Pa, and Ma) to serve as subtle or unobtrusive measures of their scales. Forty outpatients completed the MMPI under standard instructions, followed by a fake-good or fake-bad instructional set. First, we investigated the paradoxical effect found in the MMPI faking literature (in which, overall, the subtle subscale T-scores change in a direction opposite of the faking instructions) and found that not every subtle subscale shows this effect. Secondly, the subtle subscale T-scores achieved under faking conditions showed no significant relationship to their respective full-scale T-scores achieved under standard conditions. Therefore, our results do not support the Wiener–Harmon subtle subscales as subtle measures of their scales. © 2000 John Wiley & Sons, Inc. J Clin Psychol 56: 139–148, 2000.
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Abstract Bipolar disorder (BPD) is often confused with major depression, and current diagnostic questionnaires are subjective and time intensive. The aim of this study was to develop a new Bipolar Diagnosis Checklist in Chinese (BDCC) by using machine learning to shorten the Affective Disorder Evaluation scale (ADE) based on an analysis of registered Chinese multisite cohort data. In order to evaluate the importance of each item of the ADE, a case-control study of 360 bipolar disorder (BPD) patients, 255 major depressive disorder (MDD) patients and 228 healthy (no psychiatric diagnosis) controls (HCs) was conducted, spanning 9 Chinese health facilities participating in the Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder (CAFÉ-BD). The BDCC was formed by selected items from the ADE according to their importance as calculated by a random forest machine learning algorithm. Five classical machine learning algorithms, namely, a random forest algorithm, support vector regression (SVR), the least absolute shrinkage and selection operator (LASSO), linear discriminant analysis (LDA) and logistic regression, were used to retrospectively analyze the aforementioned cohort data to shorten the ADE. Regarding the area under the receiver operating characteristic (ROC) curve (AUC), the BDCC had high AUCs of 0.948, 0.921, and 0.923 for the diagnosis of MDD, BPD, and HC, respectively, despite containing only 15% (17/113) of the items from the ADE. Traditional scales can be shortened using machine learning analysis. By shortening the ADE using a random forest algorithm, we generated the BDCC, which can be more easily applied in clinical practice to effectively enhance both BPD and MDD diagnosis.
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Five validity scales derived from the Minnesota Multiphasic Personality Inventory-2 (MMPI-2), the Infrequency Scale (F), Infrequency-Psychopathology Scale (F[p]), Symptom Validity Scale (FBS), Henry-Heilbronner Index (HHI), and Response Bias Scale (RBS) were evaluated in 118 litigation patients (LPs) and 163 clinical patients (CPs). Varied statistical methods, including hierarchical logistic regression analyses, Receiver Operating Characteristic (ROC) curve, Area Under the Curve (AUC) values, and sensitivity/specificity analyses, showed that RBS performed better than the other four scales in identifying LPs. The regression analyses found RBS to be the most significant predictor of LP and CP group membership (p < .001). The effectiveness of RBS in identifying LPs, all of whom reported neuropsychological symptoms, was attributed to its development based on cognitive effort test scores.
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Personality and psychopathology features may play an important role as predisposing factors for fibromyalgia (FM). However, psychological heterogeneity of FM patients has been suggested. Based on two personality psychopathology clusters, we intend to explore psychological heterogeneity in FM patients, specifically, to identify if personality features had other psychological and psychopathological correlates. Secondarily, we also want to identify if personality features have association with health‐related correlates. The participants were female FM patients ( n = 56) between 30 and 60 years old. The instruments were: Minnesota Multiphasic Personality Inventory (MMPI‐2) content and supplementary scales, Fibromyalgia Impact Questionnaire (FIQ), and Numerical Rating Scale (NRS‐11). Multivariate analyses of variance (MANOVAs) identified that Cluster 2 ( n = 24), characterized by a combination of negative affectivity and social inhibition, presented a more disturbed profile, with several features of symptomatic behavior, general maladjustment, and important clinical problem areas. The associations of personality variables with FM impact and self‐reported pain are null, with the exception of Disconstraint scale. In conclusion, FM patients may be very different at the psychological level, concerning personality and psychopathological features that may compromise their treatment. Personality and health‐related dimensions do not seem to be associated.
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Abstract Background This study was performed for clinical study of suicide including risk factors in psychiatric patients admitted in psychiatric unit of Assiut University Hospital (AUH). Results We found that frequency of suicidal attempts is more in MDD (major depressive disorder), bipolar disorders followed by schizophrenia. And frequency of suicidal attempts in patients with multiple previous attempts before this one was higher in patients with mood disorders (53.8%) than psychotic and substance-induced disorders (32.3%, 13.8% respectively), with statistically significant difference in patients with MDD as well as there was significant treatment outcome on suicidal behavior. Conclusion The frequency of suicidal attempts is more in MDD (major depressive disorder), bipolar disorders followed by schizophrenia. There was significant severity of suicidal behavior in patients with psychotic disorders in comparison to patients with substance-induced disorder or mood disorders.
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There has been considerable controversy and research regarding sex bias in the diagnosis of personality disorders, but little has involved self-report inventories. Thus this study investigated items from the Millon Clinical Multiaxial Inventory-II (Millon, 1987), the Minnesota Multiphasic Personality Inventory (Morey, Waugh, & Blashfield, 1985), and the Personality Diagnostic Questionnaire-Revised (Hyler & Rieder, 1987). Subjects (N = 189) completed the Histrionic, Dependent, Antisocial, and Narcissistic scales from these inventories, along with the Bem Sex Role Inventory (Bem, 1974) and the Symptom Checklist-90-Revised (Derogatis, 1977). Items were considered to evidence sex or gender bias if they (a) failed to correlate with dysfunction and (b) exhibited sex or gender role differences. At least 13 items evidenced sex bias (76 items using a more liberal threshold). The majority were from Narcissistic scales; few Histrionic items evidenced sex or gender bias. Implications with respect to sex-bias assessment and item construction are discussed.
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