Although it is widely accepted that schizophrenia and other serious mental illnesses (SMI) are associated with neurocognitive difficulties, there is great variability in neurocognitive functioning across individuals. In recent years, a growing number of schizophrenia studies have utilized the concept of learning potential to explore individual variation in cognition. Learning potential refers to the ability to benefit from instruction and is measured by assessing test performance before and after training. The present study was intended to explore the cognitive characteristics associated with learning potential in people with serious mental illness. Sixty individuals with schizophrenia, bipolar or major (unipolar) depression completed a learning potential assessment using the Wisconsin Card Sorting Test (WCST) and a battery of standard cognitive measures. Based on established criteria for WCST learner subgroups, participants were categorized as high achievers, learners or non-retainers. There were several significant cognitive differences among the three learner subgroups. Most notably, individuals who were categorized as learners on the WCST showed significantly better verbal and working memory compared to non-retainers. Secondary analyses revealed that the three SMI diagnostic groups (depression, bipolar, schizophrenia) were similar in learning potential and did not differ on any of the standard cognitive measures. This study provides support for learning potential classification in schizophrenia as well as other serious mental illnesses, and indicates that learning potential may specifically be related to verbal and working memory abilities.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.schres.2006.05.012 | DOI Listing |
JMIR Res Protoc
January 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Artificial intelligence (AI)-based clinical decision support systems (CDSS) have been developed for several diseases. However, despite the potential to improve the quality of care and thereby positively impact patient-relevant outcomes, the majority of AI-based CDSS have not been adopted in standard care. Possible reasons for this include barriers in the implementation and a nonuser-oriented development approach, resulting in reduced user acceptance.
View Article and Find Full Text PDFInteract J Med Res
January 2025
Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Background: Incorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
Objective: This study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
J Med Internet Res
January 2025
Department of Radiation Oncology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Background: Primary intracranial germ cell tumors (iGCTs) are highly malignant brain tumors that predominantly occur in children and adolescents, with an incidence rate ranking third among primary brain tumors in East Asia (8%-15%). Due to their insidious onset and impact on critical functional areas of the brain, these tumors often result in irreversible abnormalities in growth and development, as well as cognitive and motor impairments in affected children. Therefore, early diagnosis through advanced screening techniques is vital for improving patient outcomes and quality of life.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Department of Gastroenterology, Affiliated Hospital of Guangdong Medical University, Zhanjiang, China.
Background: Gastrointestinal bleeding (GIB) is a severe and potentially life-threatening complication in patients with acute myocardial infarction (AMI), significantly affecting prognosis during hospitalization. Early identification of high-risk patients is essential to reduce complications, improve outcomes, and guide clinical decision-making.
Objective: This study aimed to develop and validate a machine learning (ML)-based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support.
Proc Natl Acad Sci U S A
February 2025
Molecular Ecology and Evolution Group, School of Environmental and Natural Sciences, Bangor University, Bangor LL57 2UW, United Kingdom.
Phenotypic plasticity may pave the way for rapid adaptation to newly encountered environments. Although it is often contested, there is growing evidence that initial plastic responses of ancestral populations to new environmental cues may promote subsequent adaptation. However, we do not know whether plasticity to cues present in the ancestral habitat (past-cue plasticity) can facilitate adaptation to novel cues.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!