Patients can benefit from accessible breast cancer risk information. The Gail model is a well-known means of providing risk information to patients and for guiding clinical decisions. Risk presentation often includes 5-year and life-time percent chances for a woman to develop breast cancer. How do women perceive their risks after Gail model risk assessment? This exploratory study used a randomized clinical trial design to address this question among women not previously selected for breast cancer risk. Results suggest a brief risk assessment intervention changes quantitative and comparative risk perceptions and improves accuracy. This study improves our understanding of risk perceptions by evaluating an intervention in a population not previously selected for high-risk status and measuring perceptions in a variety of formats.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1023/b:jobm.0000019852.53048.b3 | DOI Listing |
Biomimetics (Basel)
December 2024
School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China.
In this paper, a deep reinforcement learning (DRL) approach based on generative adversarial imitation learning (GAIL) and long short-term memory (LSTM) is proposed to resolve tracking control problems for robotic manipulators with saturation constraints and random disturbances, without learning the dynamic and kinematic model of the manipulator. Specifically, it limits the torque and joint angle to a certain range. Firstly, in order to cope with the instability problem during training and obtain a stability policy, soft actor-critic (SAC) and LSTM are combined.
View Article and Find Full Text PDFLiver Int
January 2025
Disease Elimination Program, Burnet Institute, Melbourne, Australia.
Proc ACM SIGSPATIAL Int Conf Adv Inf
November 2023
University of Southern California, Department of Computer Science, Los Angeles, United States.
Accessing realistic human movements (aka trajectories) is essential for many application domains, such as urban planning, transportation, and public health. However, due to privacy and commercial concerns, real-world trajectories are not readily available, giving rise to an important research area of generating synthetic but realistic trajectories. Inspired by the success of deep neural networks (DNN), data-driven methods learn the underlying human decision-making mechanisms and generate synthetic trajectories by directly fitting real-world data.
View Article and Find Full Text PDFAppetite
January 2025
Sanford Research, Center for Biobehavioral Research, 120 8th St. South, P.O. Box 2010, Fargo, ND, 58122, USA; Department of Psychiatry and Behavioral Science, School of Medicine and Health Sciences, University of North Dakota, 1919 Elm St. N, Fargo, ND, 58102-2416, USA.
Disordered eating behavior has been linked to suboptimal weight outcomes following metabolic and bariatric surgery (MBS), thereby threatening the most efficacious treatment for severe obesity. While up to 40% of patients may experience loss of control (LOC) eating following MBS, mechanisms driving this behavior are not fully understood. Preliminary evidence suggests that high levels of negative affect (NA) in the moment prompt LOC eating post-MBS; however, it remains unclear whether this momentary relationship is stable or changes over the first several years following surgery.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!