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The United States Veterans Affairs (VA) Health Care System has a strong history of conducting impactful oncology randomized clinical trials (RCTs). We developed a phase II/III RCT to test the use of metastasis-directed therapy in Veterans with oligometastatic prostate cancer (OMPC)-the first VA RCT in OMPC that leverages novel imaging and advanced radiotherapy techniques. To accomplish this, we developed a clinical trial network to conduct the study. In this manuscript, we describe several challenges we encountered in study development/conduct and our strategies to address them, with the goal of helping investigators establish robust study networks to conduct clinical trials. In the study start-up, we encountered challenges in timely site activation, and leveraged project management to maximize efficiency. Additionally, there were several changes in the clinical paradigms in imaging and treatment that led to protocol amendments to ensure maximum equipoise, recruitment, and impact of the study. Specifically, we amended the trial to add de novo OMPC patients (from initially only recurrent OMPC) and expanded the study to allow up to 10 metastases (from initially five). Finally, in order to maintain local study team engagement, we developed initiatives to maximize collaboration and add value to the overall clinical program through study participation.

Background: Since the onset of COVID-19, public health policies and public opinions changed from stringent preventive measures against spread of COVID-19 to policies accommodating life with continued, diminished risk for contracting COVID-19. Poland is a country that demonstrated severe psychological impact and negative mental health. The study aims to examine psychological impact and changes in levels of depression, anxiety, and stress among three cross-sectional samples of Polish people and COVID-19-related factors associated with adverse mental health., Methods: In total, 2324 Polish persons participated in repeated cross-sectional studies across three surveys: Survey 1 (22 to 26 March 2020), Survey 2 (21 October to 3 December 2020), and Survey 3 (3 November to 10 December 2021). Participants completed an online survey, including Impact of Event Scale-Revised (IES-R), Depression, Anxiety, and Stress Scale (DASS-21), demographics, knowledge, and concerns of COVID-19 and precautionary measures., Results: A significant reduction of IES-R scores was seen across surveys, while DASS-21 scores were significantly higher in Survey 2. There was significant reduction in the frequency of following COVID-19 news, recent COVID-19 testing, and home isolation from Survey 1 to 3. Being emale was significantly associated with higher IES-R and DASS-21 scores in Surveys 1 and 2. Student status was significantly associated with higher DASS-21 across surveys. Chills, myalgia, and fatigue were significantly associated with high IES-R or DASS-21 scores across surveys. Frequency of wearing masks and perception that mask could reduce risk of COVID-19 were significantly associated with higher IES-R and DASS-21 scores., Conclusion: Conclusions: The aforementioned findings indicate a reduction in the level of the measured subjective distress andin the frequency of checking COVID-19 news-related information across three periods during the pandemic in Poland.

Input devices such as motor-imagery brain-computer interfaces (BCIs) are often unreliable. In theory, channel coding can be used in the human-machine loop to robustly encapsulate intention through noisy input devices but standard feedforward error correction codes cannot be practically applied. We present a practical and general probabilistic user interface for binary input devices with very high noise levels. Our approach allows any level of robustness to be achieved, regardless of noise level, where reliable feedback such as a visual display is available. In particular, we show efficient zooming interfaces based on feedback channel codes for two-class binary problems with noise levels characteristic of modalities such as motor-imagery based BCI, with accuracy <75%. We outline general principles based on separating channel, line and source coding in human-machine loop design. We develop a novel selection mechanism which can achieve arbitrarily reliable selection with a noisy two-state button. We show automatic online adaptation to changing channel statistics, and operation without precise calibration of error rates. A range of visualisations are used to construct user interfaces which implicitly code for these channels in a way that it is transparent to users. We validate our approach with a set of Monte Carlo simulations, and empirical results from a human-in-the-loop experiment showing the approach operates effectively at 50-70% of the theoretical optimum across a range of channel conditions., Competing Interests: The authors have declared that no competing interests exist.