Dropout is often used as an outcome measure in clinical trials of antipsychotic medication. Previous research is inconclusive regarding (a) differences in dropout rates between first- and second-generation antipsychotic medications and (b) how trial design features reduce dropout. Meta-analysis of randomized controlled trials (RCTs) of antipsychotic medication was conducted to compare dropout rates for first- and second-generation antipsychotic drugs and to examine how a broad range of design features effect dropout. Ninety-three RCTs that met inclusion criteria were located (n = 26 686). Meta-analytic random effects models showed that dropout was higher for first- than second-generation drugs (odds ratio = 1.49, 95% confidence interval: 1.31-1.66). This advantage persisted after removing study arms with excessively high dosages, in flexible dose studies, studies of patients with symptom exacerbation, nonresponder patients, inpatients, and outpatients. Mixed effects models for meta-analysis were used to identify design features that effected dropout and develop formulae to derive expected dropout rates based on trial design features, and these assigned a pivotal role to duration. Collectively, dropout rates are lower for second- than first-generation antipsychotic drugs and appear to be partly explained by trial design features thus providing direction for future trial design.
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http://dx.doi.org/10.1093/schbul/sbn005 | DOI Listing |
Trends Cogn Sci
January 2025
Department of Psychology, Biological Psychology, University of Cologne, Cologne, Germany. Electronic address:
Multi-line electronic gambling machines (EGMs) are strongly associated with problem gambling. Dopamine (DA) plays a central role in substance-use disorders, which share clinical and behavioral features with disordered gambling. The structural design features of multi-line EGMs likely lead to the elicitation of various dopaminergic effects within their nested anticipation-outcome structure.
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State Key Laboratory of New Pharmaceutical Preparations and Excipients, Key Laboratory of Medicinal Chemistry and Molecular Diagnosis of the Ministry of Education, Key Laboratory of Analytical Science and Technology of Hebei Province, College of Chemistry and Materials Science, Hebei University 071002 Baoding, PR China. Electronic address:
In this study, a Co doped polyhedral carbon skeleton (Co CN) was prepared by nitrogen carbonization using ZIF-67 as a precursor. The Co CN features a rough surface with excellent electrical conductivity, and the Co atoms exhibit unique catalytic properties. Based on these characteristics, we used Co CN as a carrier to load Au nanoparticles (NPs) onto its surface through the linkage and reduction effects of polyoxometalates (POMs).
View Article and Find Full Text PDFNeural Netw
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Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
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Nantes Université, Centrale Nantes, CNRS, LS2N, UMR 6004, F-44000 Nantes, France.
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View Article and Find Full Text PDFAdv Mater
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State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry, Jilin University, Changchun, 130012, China.
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