The Million Clinical Multiaxial Inventory (MCMI versions I, II, and III) includes a scale to assess drug use problems, Scale T-Drug Dependence. Detailed drug use data from a sample of 659 known drug users along with MCMI-II results were examined to determine the operating characteristics of the MCMI-II drug dependence scale. Operating characteristics, sensitivity, specificity, positive predictive power, negative predictive power, and overall diagnostic power were calculated for base rate cutoffs and for the number of prototypic items endorsed to determine the diagnostic efficiency of Scale T-Drug Dependence in identifying regular drug users. Prototypic item cutoffs provided higher levels of diagnostic and positive predictive power than did the standard base rate cutoffs.
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http://dx.doi.org/10.3109/10826089709039373 | DOI Listing |
JCO Precis Oncol
January 2025
Translational Research Support Office, National Cancer Center Hospital East, Chiba, Japan.
Purpose: Human epidermal growth factor receptor 2 (HER2)-targeted therapies have shown promise in treating -amplified metastatic colorectal cancer (mCRC). Identifying optimal biomarkers for treatment decisions remains challenging. This study explores the potential of artificial intelligence (AI) in predicting treatment responses to trastuzumab plus pertuzumab (TP) in patients with -amplified mCRC from the phase II TRIUMPH trial.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer and Information Science, Konstanz University, Konstanz, Baden-Württemberg, Germany.
A major challenge of our time is reducing disparities in access to and effective use of digital technologies, with recent discussions highlighting the role of AI in exacerbating the digital divide. We examine user characteristics that predict usage of the AI-powered conversational agent ChatGPT. We combine behavioral and survey data in a web tracked sample of N = 1376 German citizens to investigate differences in ChatGPT activity (usage, visits, and adoption) during the first 11 months from the launch of the service (November 30, 2022).
View Article and Find Full Text PDFProc Natl Acad Sci U S A
January 2025
Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130.
Task-free brain activity affords unique insight into the functional structure of brain network dynamics and has been used to identify neural markers of individual differences. In this work, we present an algorithmic optimization framework that directly inverts and parameterizes brain-wide dynamical-systems models involving hundreds of interacting neural populations, from single-subject M/EEG time-series recordings. This technique provides a powerful neurocomputational tool for interrogating mechanisms underlying individual brain dynamics ("precision brain models") and making quantitative predictions.
View Article and Find Full Text PDFJ Clin Exp Dent
December 2024
Faculty of Sciencies of Health. Universidad Nacional del Callao.
Background: To evaluate the performance of different prediction models based on machine learning to predict the presence of early childhood caries.
Material And Methods: Cross-sectional analytical study. The sociodemographic and clinical data used came from a sample of 186 children aged 3 to 6 years and their respective parents or guardians treated at a Hospital in Ica, Peru.
Am J Transl Res
December 2024
Department of Gynecology, Suzhou Ninth People's Hospital Suzhou 215200, Jiangsu, China.
Objective: To investigate the factors influencing recurrence following laparoscopic conservative surgery in patients with ovarian endometriosis (OEM) and to develop a predictive model.
Methods: In this retrospective study, the clinical data from 212 OEM patients who underwent laparoscopic conservative surgery at Suzhou Ninth People's Hospital from May 2013 to December 2021 were meticulously reviewed. According to disease recurrence over a 2-year follow-up period, the patients were divided into a recurrence group and a non-recurrence group.
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