Objective: This study examines the effects of a mammography decision intervention on perceived susceptibility to breast cancer (PSBC) and emotion and investigates how these outcomes predict mammography intentions.
Design: Randomised between-subjects online experiment. Participants were stratified into two levels of risk. Within each stratum, conditions included a basic information condition and six decision intervention conditions that included personalised risk estimates and varied according to a 2 (amount of information: brief vs. extended) × 3 (format: expository vs. untailored exemplar vs. tailored exemplar) design. Participants included 2465 US women ages 35-49.
Main Outcome Measures: PSBC as a percentage, PSBC as a frequency, worry, fear and mammography intentions.
Results: The intervention resulted in significant reductions in PSBC as a percentage for women in both strata and significant increases in worry and fear for women in the upper risk stratum. Of the possible mediators examined, only PSBC as a percentage was a consistent mediator of the effect of the intervention on mammography intentions.
Conclusion: The results provide insight into the mechanism of action of the intervention by showing that PSBC mediated the effects of the intervention on mammography intentions.
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http://dx.doi.org/10.1080/08870446.2017.1387261 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
J Imaging Inform Med
January 2025
Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
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 PDFInt Urol Nephrol
January 2025
Department of Urology and Urosurgery, Medical Faculty Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Baden-Württemberg, Germany.
Purpose: To identify prognostic factors for overall survival (OS) and develop a prognostic score in patients receiving docetaxel in metastatic castration-resistant prostate cancer (mCRPC).
Methods: Retrospective analysis was conducted on mCRPC patients treated with docetaxel at a German tertiary center between March 2010 and November 2023. Prognostic clinical and laboratory factors were analyzed using uni- and multivariable logistic regression.
Eur J Nucl Med Mol Imaging
January 2025
The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
Purpose: The study explores the role of multimodal imaging techniques, such as [F]F-PSMA-1007 PET/CT and multiparametric MRI (mpMRI), in predicting the ISUP (International Society of Urological Pathology) grading of prostate cancer. The goal is to enhance diagnostic accuracy and improve clinical decision-making by integrating these advanced imaging modalities with clinical variables. In particular, the study investigates the application of few-shot learning to address the challenge of limited data in prostate cancer imaging, which is often a common issue in medical research.
View Article and Find Full Text PDFCommun Eng
January 2025
School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore.
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.
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