Plos One
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Observational Study
Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. ⋯ Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.
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Early differentiation between emergency department (ED) patients with and without corona virus disease (COVID-19) is very important. Chest CT scan may be helpful in early diagnosing of COVID-19. We investigated the diagnostic accuracy of CT using RT-PCR for SARS-CoV-2 as reference standard and investigated reasons for discordant results between the two tests. ⋯ The accuracy of chest CT in symptomatic ED patients is high, but used as a single diagnostic test, CT can not safely diagnose or exclude COVID-19. However, CT can be used as a quick tool to categorize patients into "probably positive" and "probably negative" cohorts.
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The teaching of professional roles in medical education is an interdisciplinary concern. However, surgeons require specific standards of professionalism for certain context-based situations. In addition to communication, studies require collaboration, leadership, error-/conflict-management, patient-safety and decision-making as essential competencies for surgeons. ⋯ Overall, the involvement of surgery in teaching the competencies of the Collaborator and Manager/Leader is currently low. However, there are indications of a curricular development towards explicit teaching of these roles in surgery. Moreover, implicitly taught roles are numerous, which indicates a beginning awareness of professional roles.
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Muscle depletion and sarcopenic obesity are related to a higher morbimortality risk in chronic kidney disease (CKD). We evaluated bed-side measures/indexes associated with low muscle mass, sarcopenia, obesity, and sarcopenic obesity in CKD and proposed cutoffs for each parameter. Sarcopenia was diagnosed according to the European Working Group on Sarcopenia in Older People revised consensus applying dual energy X-ray absorptiometry (DXA) and hand grip strength (HGS), and obesity according to the International Society for Clinical Densitometry. ⋯ The cutoffs (sensibility and specificity, respectively) for women were AFFM≤15.87 (90%; 96%), CC≤35.5 (76%; 94%), FMI>12.58 (100%; 93%), and WC/H>0.66 (91%; 84%); and for men, AFFM≤21.43 (98%; 84%), CC≤37 (88%; 69%), FMI>8.82 (93%; 88%), and WC/H>0.60 (95%; 80%). Sensibility and specificity for sarcopenia diagnosis were for AFFM+HGS in women 85% and 99% and in men, 100% and 99%; for CC+HGS in women 85% and 99% and in men, 100% and 100%; and for sarcopenic obesity were for FMI+AFFM in women 75% and 97% and in men, 75% and 95%. The tested bed-side measures/indexes presented excellent performance.
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To "put oneself in the place of other road users" may improve understanding of the global traffic situation. It should be useful enabling drivers to anticipate and detect obstacles in time to prevent accidents to other road users, especially those most vulnerable. We created a pioneering Hazard Perception and Prediction test to explore this skill in different road users (pedestrians, cyclists and drivers), with videos recorded in naturalistic scenarios: walking, riding a bicycle and driving a car. ⋯ The results showed that the holistic test had acceptable psychometric properties (Cronbach's alpha = .846). The test was able to discriminate between the different conditions manipulated: a) between traffic hazards recorded from different perspectives: walking, riding a bicycle and driving a car; b) between participants with different user profiles: pedestrians, cyclists and drivers; c) between the two test blocks: the first evaluation only and the second combining evaluation with this complex intervention. We found modal bias effects in both Hazard Perception and Prediction; and in Risk Estimation.