Notes

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Finally, we address the esophageal complications of LP.Medical students demonstrate their passion for participating in and improving health care both within and outside the classroom. As the COVID-19 pandemic swept across the world, medical students in the United States engaged in student-led service-learning initiatives to contribute to medicine and their local communities, including collecting and distributing personal protective equipment, creating and translating pandemic-related educational materials, and providing childcare for frontline workers. Their impact was recognized and appreciated. Service learning is an education method that incorporates community outreach with didactic coursework and student reflection. In this Invited Commentary, the authors argue for including service learning as a required component in the medical school curriculum to provide students with the tools and support to be advocates and leaders within society, as no such curriculum currently exists. The authors also discuss the history of medical student-led service-learning efforts through to the present day, barriers to implementing and sustaining student-led service-learning initiatives, and solutions to prepare students for service-learning initiatives.
Recently, artificial intelligence technologies and machine learning methods have offered attractive prospects to design and manage crisis response processes, especially in suicide crisis management. In other domains, most algorithms are based on big data to help diagnose and suggest rational treatment options in medicine. But data in psychiatry are related to behavior and clinical evaluation. They are more heterogeneous, less objective, and incomplete compared to other fields of medicine. Consequently, the use of psychiatric clinical data may lead to less accurate and sometimes impossible-to-build algorithms and provide inefficient digital tools. In this case, the Bayesian network (BN) might be helpful and accurate when constructed from expert knowledge. Medical Companion is a government-funded smartphone application based on repeated questions posed to the subject and algorithm-matched advice to prevent relapse of suicide attempts within several months.

Our paper aims to present our development of a BN acalTrials.gov NCT03975881; https//clinicaltrials.gov/ct2/show/NCT03975881.
An artificial intelligence (AI)-assisted contouring system benefits radiation oncologists by saving time and improving treatment accuracy. Yet, there is much hope and fear surrounding such technologies, and this fear can manifest as resistance from health care professionals, which can lead to the failure of AI projects.

The objective of this study was to develop and test a model for investigating the factors that drive radiation oncologists' acceptance of AI contouring technology in a Chinese context.

A model of AI-assisted contouring technology acceptance was developed based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model by adding the variables of perceived risk and resistance that were proposed in this study. The model included 8 constructs with 29 questionnaire items. A total of 307 respondents completed the questionnaires. Structural equation modeling was conducted to evaluate the model's path effects, significance, and fitness.

The overall fitness indices for the model weption among physicians in a Chinese context.
Justice-involved youth are especially vulnerable to mental health distress, substance misuse, and risky sexual activity, amplifying the need for evidence-based programs (EBPs). Yet, uptake of EBPs in the justice system is challenging because staff training is costly in time and effort. Hence, justice-involved youth experience increasing health disparities despite the availability of EBPs.

To counter these challenges, this study develops and pilot-tests a prototype of a technology-based training tool that teaches juvenile justice staff to deliver a uniquely tailored EBP for justice-involved youth-PHAT (Preventing HIV/AIDS Among Teens) Life. PHAT Life is a comprehensive sex education, mental health, and substance use EBP collaboratively designed and tested with guidance from key stakeholders and community members. The training tool addresses implementation barriers that impede uptake and sustainment of EBPs, including staff training and support and implementation costs.

Staff (n=11) from two juvenile justtrolled trial. Ultimately, this study will provide a scalable option for disseminating an EBP and offers a more cost-effective and sustainable way to train staff in an EBP.
Overall, the results from this pilot test demonstrate feasibility and support continuing efforts toward completing the training tool and evaluating it within a fully powered randomized controlled trial. Ultimately, this study will provide a scalable option for disseminating an EBP and offers a more cost-effective and sustainable way to train staff in an EBP.
COVID-19 is a major public health concern. Given the extent of the pandemic, it is urgent to identify risk factors associated with disease severity. More accurate prediction of those at risk of developing severe infections is of high clinical importance.

Based on the UK Biobank (UKBB), we aimed to build machine learning models to predict the risk of developing severe or fatal infections, and uncover major risk factors involved.

We first restricted the analysis to infected individuals (n=7846), then performed analysis at a population level, considering those with no known infection as controls (ncontrols=465,728). Hospitalization was used as a proxy for severity. A total of 97 clinical variables (collected prior to the COVID-19 outbreak) covering demographic variables, comorbidities, blood measurements (eg, hematological/liver/renal function/metabolic parameters), anthropometric measures, and other risk factors (eg, smoking/drinking) were included as predictors. We also constructed a simplified (lite) prs, and cardiometabolic abnormalities may predispose to poorer outcomes. The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.
We identified numerous baseline clinical risk factors for severe/fatal infection by XGboost. For example, age, central obesity, impaired renal function, multiple comorbidities, and cardiometabolic abnormalities may predispose to poorer outcomes. selleck chemicals The prediction models may be useful at a population level to identify those susceptible to developing severe/fatal infections, facilitating targeted prevention strategies. A risk-prediction tool is also available online. Further replications in independent cohorts are required to verify our findings.
The COVID-19 pandemic has required clinicians to pivot to offering services via telehealth; however, it is unclear which patients (users of care) are equipped to use digital health. This is especially pertinent for adults managing chronic diseases, such as obesity, hypertension, and diabetes, which require regular follow-up, medication management, and self-monitoring.

The aim of this study is to measure the trends and assess factors affecting health information technology (HIT) use among members of the US population with and without cardiovascular risk factors.

We used serial cross-sectional data from the National Health Interview Survey for the years 2012-2018 to assess trends in HIT use among adults, stratified by age and cardiovascular risk factor status. We developed multivariate logistic regression models adjusted for age, sex, race, insurance status, marital status, geographic region, and perceived health status to assess the likelihood of HIT use among patients with and without cardiovascular disly to use HIT compared to adults without high school education among people with multiple cardiovascular risk factors, one cardiovascular risk factor, or no cardiovascular risk factors, respectively.

Over 2012-2018, HIT use increased nationally, with greater use noted among younger and higher educated US adults. Targeted strategies are needed to engage wider age, racial, education, and socioeconomic groups by lowering barriers to HIT access and use.
Over 2012-2018, HIT use increased nationally, with greater use noted among younger and higher educated US adults. Targeted strategies are needed to engage wider age, racial, education, and socioeconomic groups by lowering barriers to HIT access and use.
Social communication via instant messaging (IM) and social networking (SN) apps makes up a large part of the time that smartphone users spend on their devices. Previous research has indicated that the excessive use of these apps is positively associated with problematic smartphone use behaviors. In particular, image-based SN apps, such as Instagram (Facebook Inc) and Snapchat (Snap Inc), have been shown to exert stronger detrimental effects than those exerted by traditional apps, such as Facebook (Facebook Inc) and Twitter (Twitter Inc).

In this study, we investigated the correlation between individuals' tendencies toward smartphone use disorder (SmUD) and objective measures of the frequency of smartphone usage. Additionally, we put to test the hypothesis that the pathway linking the frequency of actual smartphone usage to self-reported tendencies toward SmUD was mediated by the increased frequency of IM and SN app usage.

We recruited a sample of 124 adult smartphone users (females 78/124, 62.9%; age meem to be more strongly associated with problematic smartphone behaviors compared to IM apps and traditional SN apps, such as Facebook.
Our novel results shed light on the factors that promote SmUD tendencies and essentially indicate that image-based SN apps seem to be more strongly associated with problematic smartphone behaviors compared to IM apps and traditional SN apps, such as Facebook.
Intraoperative hypotension has an adverse impact on postoperative outcomes. However, it is difficult to predict and treat intraoperative hypotension in advance according to individual clinical parameters.

The aim of this study was to develop a prediction model to forecast 5-minute intraoperative hypotension based on the weighted average ensemble of individual neural networks, utilizing the biosignals recorded during noncardiac surgery.

In this retrospective observational study, arterial waveforms were recorded during noncardiac operations performed between August 2016 and December 2019, at Seoul National University Hospital, Seoul, South Korea. We analyzed the arterial waveforms from the big data in the VitalDB repository of electronic health records. We defined 2s hypotension as the moving average of arterial pressure under 65 mmHg for 2 seconds, and intraoperative hypotensive events were defined when the 2s hypotension lasted for at least 60 seconds. We developed an artificial intelligence-enabled process, named short-term event prediction in the operating room (STEP-OP), for predicting short-term intraoperative hypotension.

The study was performed on 18,813 subjects undergoing noncardiac surgeries. Deep-learning algorithms (convolutional neural network [CNN] and recurrent neural network [RNN]) using raw waveforms as input showed greater area under the precision-recall curve (AUPRC) scores (0.698, 95% CI 0.690-0.705 and 0.706, 95% CI 0.698-0.715, respectively) than that of the logistic regression algorithm (0.673, 95% CI 0.665-0.682). STEP-OP performed better and had greater AUPRC values than those of the RNN and CNN algorithms (0.716, 95% CI 0.708-0.723).

We developed STEP-OP as a weighted average of deep-learning models. STEP-OP predicts intraoperative hypotension more accurately than the CNN, RNN, and logistic regression models.

ClinicalTrials.gov NCT02914444; https//clinicaltrials.gov/ct2/show/NCT02914444.
ClinicalTrials.gov NCT02914444; https//clinicaltrials.gov/ct2/show/NCT02914444.
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