The predictive value of the Oxfordshire Community Stroke Project ischemic stroke classification for acute stroke complications, therapeutic interventions and disability at discharge was investigated in 297 consecutive first-ever acute stroke patients. More than one medical complication (odds ratio, OR = 2.2), fever (OR = 2.5) and dependency (Rankin grade >2) at discharge (OR = 2.3) were more frequent in intracerebral hemorrhage patients. Fever and urinary tract infections were the most common complications among ischemic stroke patients. Both were more frequent in total anterior circulation infarct (TACI) patients (OR = 11.5 and OR = 3.7). Neurological deterioration was observed in about 10% of TACI and posterior circulation infarct (POCI) patients. Dependency at discharge was more frequent in TACI patients (OR = 10.3). Logistic regression analysis identified ischemic stroke subgroups (OR = 8.4) and medical complications (OR = 3.8) as predictors of poor outcome (Rankin score ≥4). A clinical classification is useful to predict possible medical and neurological complications in the acute phase, death and dependency at discharge.

The most common variables used to compute probability of death algorithms were not capable of predicting health-related quality of life in survivors to pediatric intensive care. The preadmission health-related quality of life and trauma admissions are important variables to predict change in the health-related quality of life of children surviving to pediatric intensive care.

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The International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) COVID-19 dataset is one of the largest international databases of prospectively collected clinical data on people hospitalized with COVID-19. This dataset was compiled during the COVID-19 pandemic by a network of hospitals that collect data using the ISARIC-World Health Organization Clinical Characterization Protocol and data tools. The database includes data from more than 705,000 patients, collected in more than 60 countries and 1,500 centres worldwide. Patient data are available from acute hospital admissions with COVID-19 and outpatient follow-ups. The data include signs and symptoms, pre-existing comorbidities, vital signs, chronic and acute treatments, complications, dates of hospitalization and discharge, mortality, viral strains, vaccination status, and other data. Here, we present the dataset characteristics, explain its architecture and how to gain access, and provide tools to facilitate its use.

Abstract-This paper presents a user study that investigates 2D and 3D visualizations of bar charts in geographic maps. The task to be solved by the participants in this study required estimation of the ratio of two different spatial distance measures and relative ranking among potential candidates. The results of this experiment show that subjects were equally fast and accurate when using both the 2D and 3D visualizations. Visual discomfort was reported by almost half of the test population, but performance was not affected. Our study also showed that frequent game players did not benefit more from a 3D visualization than inexperienced game-players.