Psychological disorders frequently manifest during adolescence. Because of the multifactorial influencing factors, the courses of the diseases are heterogeneous, from relapsing-remitting to chronic. This study investigated whether the level of structural integration of the Operationalized Psychodynamic Diagnostics in Childhood and Adolescence (OPD-CA) correlates with later symptomatic burden. This long-term study assessed the levels of structural integration according to the OPD-CA of 60 adolescents (mean age = 15.6; = 0.9). Seven years later, we then measured symptomatic burden (SCID axis I and II) and overall burden (GAF, BSI-GSI) (73.3 % follow-up participation rate). The results showed high correlations between deficient structural integration in adolescence and later symptoms and overall burden in early adulthood. The follow-up examination after a 7-year time period showed significant correlations, which argue for the predictive value of structural integration. This suggests that early specific treatment, e.g., in the form of intensive psychotherapy, be urgently recommended in order to influence this course.

Download full-text PDF

Source
http://dx.doi.org/10.1024/1422-4917/a000656DOI Listing

Publication Analysis

Top Keywords

structural integration
20
symptomatic burden
8
integration
5
[levels structural
4
integration adolescents
4
adolescents relationship
4
relationship mental
4
mental disorders
4
disorders longitudinal
4
longitudinal study]
4

Similar Publications

The biodiversity of ice-free Antarctica database.

Ecology

January 2025

Securing Antarctica's Environmental Future, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia.

Antarctica is one of Earth's most untouched, inhospitable, and poorly known regions. Although knowledge of its biodiversity has increased over recent decades, a diverse, wide-ranging, and spatially explicit compilation of the biodiversity that inhabits Antarctica's permanently ice-free areas is unavailable. This absence hinders both Antarctic biodiversity research and the integration of Antarctica in global biodiversity-related studies.

View Article and Find Full Text PDF

Multi-class Classification of Retinal Eye Diseases from Ophthalmoscopy Images Using Transfer Learning-Based Vision Transformers.

J Imaging Inform Med

January 2025

College of Engineering, Department of Computer Engineering, Koç University, Rumelifeneri Yolu, 34450, Sarıyer, Istanbul, Turkey.

This study explores a transfer learning approach with vision transformers (ViTs) and convolutional neural networks (CNNs) for classifying retinal diseases, specifically diabetic retinopathy, glaucoma, and cataracts, from ophthalmoscopy images. Using a balanced subset of 4217 images and ophthalmology-specific pretrained ViT backbones, this method demonstrates significant improvements in classification accuracy, offering potential for broader applications in medical imaging. Glaucoma, diabetic retinopathy, and cataracts are common eye diseases that can cause vision loss if not treated.

View Article and Find Full Text PDF

Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.

View Article and Find Full Text PDF

Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model.

View Article and Find Full Text PDF

MAI-TargetFisher: A proteome-wide drug target prediction method synergetically enhanced by artificial intelligence and physical modeling.

Acta Pharmacol Sin

January 2025

Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.

Computational target identification plays a pivotal role in the drug development process. With the significant advancements of deep learning methods for protein structure prediction, the structural coverage of human proteome has increased substantially. This progress inspired the development of the first genome-wide small molecule targets scanning method.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!