Journal of medical Internet research
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J. Med. Internet Res. · Nov 2020
ReviewVaping-Related Mobile Apps Available in the Google Play Store After the Apple Ban: Content Review.
In response to health concerns about vaping devices (eg, youth nicotine use, lung injury), Apple removed 181 previously approved vaping-related apps from the App Store in November 2019. This policy change may lessen youth exposure to content that glamorizes vaping; however, it may also block important sources of information and vaping device control for adults seeking to use vaping devices safely. ⋯ The majority of vaping-related apps in the Google Play Store had features either to help users continue vaping, such as information for modifying devices, or to maintain interest in vaping. Few apps were for controlling device settings or assisting with quitting smoking or vaping. Assuming that these Google Play Store apps were similar in content to the Apple App Store apps that were removed, it appears that Apple's ban would have a minimal effect on people who vape with the intention of quitting smoking or who are seeking information about safer vaping via mobile apps.
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J. Med. Internet Res. · Nov 2020
Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation.
Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients' prognosis early and administer precise treatment are of great significance. ⋯ The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.
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J. Med. Internet Res. · Nov 2020
An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study.
Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the COVID-19 pandemic. Although several scoring methods have been introduced, many require laboratory or radiographic findings that are not always easily available. ⋯ We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among patients with COVID-19.
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J. Med. Internet Res. · Nov 2020
Impact of Remote Consultations on Antibiotic Prescribing in Primary Health Care: Systematic Review.
There has been growing international interest in performing remote consultations in primary care, particularly amidst the current COVID-19 pandemic. Despite this, the evidence surrounding the safety of remote consultations is inconclusive. The appropriateness of antibiotic prescribing in remote consultations is an important aspect of patient safety that needs to be addressed. ⋯ There is insufficient evidence to confidently conclude that remote consulting has a significant impact on antibiotic prescribing in primary care. However, studies indicating higher prescribing rates in remote consultations than in face-to-face consultations are a concern. Further, well-conducted studies are needed to inform safe and appropriate implementation of remote consulting to ensure that there is no unintended impact on antimicrobial resistance.
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J. Med. Internet Res. · Nov 2020
A Patient Self-Checkup App for COVID-19: Development and Usage Pattern Analysis.
Clear guidelines for a patient with suspected COVID-19 infection are unavailable. Many countries rely on assessments through a national hotline or telecommunications, but this only adds to the burden of an already overwhelmed health care system. In this study, we developed an algorithm and a web application to help patients get screened. ⋯ We developed an expert-opinion-based algorithm and web-based application for screening patients. This innovation can be helpful in circumstances where information on a novel disease is insufficient and may facilitate efficient medical resource allocation.