Purpose: Decision-making is the process of forming preferences for possible options, selecting and executing actions, and evaluating the outcome. This study used the Iowa Gambling Task (IGT) and the Prospect Valence Learning (PVL) model to investigate deficits in risk-reward related decision-making in patients with chronic schizophrenia, and to identify decision-making processes that contribute to poor IGT performance in these patients.
Materials And Methods: Thirty-nine patients with schizophrenia and 31 healthy controls participated. Decision-making was measured by total net score, block net scores, and the total number of cards selected from each deck of the IGT. PVL parameters were estimated with the Markov chain Monte Carlo sampling scheme in OpenBugs and BRugs, its interface to R, and the estimated parameters were analyzed with the Mann-Whitney U-test.
Results: The schizophrenia group received significantly lower total net scores compared to the control group. In terms of block net scores, an interaction effect of group × block was observed. The block net scores of the schizophrenia group did not differ across the five blocks, whereas those of the control group increased as the blocks progressed. The schizophrenia group obtained significantly lower block net scores in the fourth and fifth blocks of the IGT and selected cards from deck D (advantageous) less frequently than the control group. Additionally, the schizophrenia group had significantly lower values on the utility-shape, loss-aversion, recency, and consistency parameters of the PVL model.
Conclusion: These results indicate that patients with schizophrenia experience deficits in decision-making, possibly due to failure in learning the expected value of each deck, and incorporating outcome experiences of previous trials into expectancies about options in the present trial.
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http://dx.doi.org/10.2147/NDT.S103821 | DOI Listing |
BMC Public Health
January 2025
Henan Medical Communication and Project Forward Center, No. 6, Xueli Road, Zhengdong New District, Zhengzhou, 450000, Henan, China.
Background: During the COVID-19 pandemic, the social distancing has significantly affected the healthy lifestyle behaviors of residents. China ended social distancing on January 8, 2023, and the healthy lifestyle behaviors of residents after this time are unclear. The goal of this study was to evaluate the differences in healthy lifestyle behaviors between Chinese urban and rural residency after the termination of social distancing.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Nutrition, School of Public Health, Zabol University of Medical Sciences, Bagheri St., Shahid Rajaei St., Zabol, 9861615881, Iran.
Knee osteoarthritis (KOA) is a prevalent chronic condition characterized by inflammation and oxidative stress, particularly in individuals over 40. Dietary factors, specifically dietary acid load (DAL), may influence these pathological processes. However, the relationship between DAL and inflammatory markers, oxidative stress, and clinical features in patients with KOA remains unexplored.
View Article and Find Full Text PDFUltrasound Med Biol
January 2025
Department of Circulation and Medical Imaging, Norwegian University of Science and Technology - NTNU, Trondheim, Norway; Health Research, SINTEF, Trondheim, Norway.
Objective: To develop and compare methods to automatically estimate regional ultrasound image quality for echocardiography separate from view correctness.
Methods: Three methods for estimating image quality were developed: (i) classic pixel-based metric: the generalized contrast-to-noise ratio (gCNR), computed on myocardial segments (region of interest) and left ventricle lumen (background), extracted by a U-Net segmentation model; (ii) local image coherence: the average local coherence as predicted by a U-Net model that predicts image coherence from B-mode ultrasound images at the pixel level; (iii) deep convolutional network: an end-to-end deep-learning model that predicts the quality of each region in the image directly. These methods were evaluated against manual regional quality annotations provided by three experienced cardiologists.
Introduction: The occurrence of Gleason grade group upgrading (GGU) significantly impacts both treatment strategy development. We aim to develop an optimal predictive model to assess the risk of GGU in patients with localized prostate cancer (PCa), by comparing traditional logistic regression (LR) with seven machine learning algorithms.
Methods: A retrospective collection of clinical data was conducted on patients who underwent RP at Wuhan Central Hospital (January 2017 to December 2023, n=177) and Jiangxi Cancer Hospital (July 2019 to February 2024, n=87).
Clin Epigenetics
January 2025
Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
Alcohol consumption is an important risk factor for multiple diseases. It is typically assessed via self-report, which is open to measurement error through recall bias. Instead, molecular data such as blood-based DNA methylation (DNAm) could be used to derive a more objective measure of alcohol consumption by incorporating information from cytosine-phosphate-guanine (CpG) sites known to be linked to the trait.
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