The factor structure of DSM-5 posttraumatic stress disorder (PTSD) has been extensively debated, with evidence supporting the recently proposed seven-factor hybrid model. However, few studies examining PTSD symptom structure have assessed the implications of these proposed models on diagnostic criteria and PTSD prevalence. In the present study, we examined seven alternative DSM-5 PTSD models within a confirmatory factor analysis (CFA), using the Child PTSD Symptom Scale-Self-Report for DSM-5 (CPSS-5). Additionally, we generated prevalence rates for each of the seven models by using a symptom-based diagnostic algorithm and assessed whether substance abuse, depression, anxiety symptoms, and daily functioning were differentially associated with PTSD depending on the model used to derive the diagnosis. Participants were 317 adolescents aged 13-17 years (M = 15.93, SD = 1.23) who had experienced a DSM-5 Criterion A trauma and/or childhood adversity. The CFA results showed good fit indices for all models, with the seven-factor hybrid model presenting the best fit. The rates of PTSD diagnosis varied according to each model. The four-factor DSM-5 model presented the highest rate (30.6%), and the seven-factor hybrid model presented the lowest rate (17.4%). Similar to the CFA analysis, the inclusion criteria for the diagnosis based on the hybrid model also presented the strongest associations with daily functional impairment, odds ratio (OR) = 1.48, 95% CI [1.25, 1.75]; and adverse childhood experiences, OR = 1.46, 95% CI [1.16, 1.82]. Research and clinical implications of these results are discussed, and suggestions for future investigation are presented.
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BMC Health Serv Res
January 2025
School of Humanities and Social Sciences, Beihang University, No. 37 Xueyuan Road, Beijing, 100191, China.
Background: To address the health inequity caused by decentralized management, China has introduced a provincial pooling system for urban employees' basic medical insurance. This paper proposes a research framework to evaluate similar policies in different contexts. This paper adopts a mixed-methods approach to more comprehensively and precisely capture the causal effects of the policy.
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January 2025
Department of Wildlife Fisheries and Aquaculture, College of Forest Resources, Mississippi State University, Mississippi State, MS, 39762-9690, USA.
This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies.
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January 2025
Department of Oncology, The First Affiliated Hospital of Yangtze University, Jingzhou, Hubei, China.
Exploring the potential of advanced artificial intelligence technology in predicting microsatellite instability (MSI) and Ki-67 expression of endometrial cancer (EC) is highly significant. This study aimed to develop a novel hybrid radiomics approach integrating multiparametric magnetic resonance imaging (MRI), deep learning, and multichannel image analysis for predicting MSI and Ki-67 status. A retrospective study included 156 EC patients who were subsequently categorized into MSI and Ki-67 groups.
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January 2025
Department of Physics, Khalifa University of Science and Technology, 127788, Abu Dhabi, United Arab Emirates.
In this study, biopolymer composites based on chitosan (CS) with enhanced optical properties were functionalized using Manganese metal complexes and black tea solution dyes. The results indicate that CS with Mn-complexes can produce polymer hybrids with high absorption, high refractive index and controlled optical band gaps, with a significant reduction from 6.24 eV to 1.
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January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
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