ABSTRACT
Introduction: The coronavirus disease 2019 (COVID-19) has brought excessive patients in emergency departments. Several COVID-19 prediction scores have been developed to aid in the patient disposition of emergency physicians. This study aimed to validate different COVID-19 prediction scores. Method: ???DynaMed??? was used to retrieve high-quality COVID-19 prediction scores for the evaluation of in-hospital mortality rate. SEIMC score, 4C-Mortality score, SOARS score, and Veterans Health Administration COVID-19 (VACO) Index were selected. A retrospective, single-center study was done on elderly patients hospitalized for COVID-19 from May 2021 to July 2021 in MacKay Memorial Hospital. Patients who were (I) negative for COVID-19 examination, (II) aged 65 years old, (III) previously infected with COVID-19 and de-isolated (IV) hospital-acquired COVID-19 infection, (V) not admitted for hospitalization, and (VI) with missing of demographic characteristics were excluded. The area under the receiver operating characteristic curves (AUC) was computed to predict the in-hospital mortality rate. Result: Of 66,090 patients who underwent COVID-19 examination in MacKay Memorial Hospital, 133 patients were included in this study, with 26 deceased patients (19.5%). Among included patients, the median age was 74.38 years and 53% patients were male. Of the selected COVID-19 prediction scores, 4C-Mortality Score (AUC = 0.8), SEIMC score (AUC = 0.75), and SOARS score (AUC = 0.72) contained a good prognostic value, with an AUC 0.70. VACO index demonstrated less predictive value (AUC = 0.61). Conclusion: COVID-19 prediction scores were validated, and it was found that 4C-Mortality Score, SEIMC score, and SOARS score performed well in predicting the in-hospital mortality rate of elderly patients with COVID-19, and 4C-Mortality score is best appreciated.
ABSTRACT
What are patterns of gender and authorship in urban land science? Our bibliometric analysis shows that the proportion of women shrinks among highly productive, impactful, and senior authors, akin to a pyramid shape. First, women are only one in ten researchers with an h-index above the 95th percentile. Second, women are first authors on 20% of all influential papers cited more than one hundred times. Third, women publish less frequently (1.6 papers/year) than men (2.2). Fourth, women have shorter career lengths (9.4 years) than men (11.8). Since the 2000s, citation rates for women and men have converged. For the generation starting careers since 2016, the proportion of women with an h-index above the 90th percentile increased to 25%. During the Covid-19 pandemic, there was a 51% increase in productivity for women. Despite these changes, gender disparities in urban land science are most pronounced among the most productive and impactful authors.
ABSTRACT
The recent outbreak of the novel Coronavirus Disease (COVID-19) has given rise to diverse health issues due to its high transmission rate and limited treatment options. Almost the whole world, at some point of time, was placed in lock-down in an attempt to stop the spread of the virus, with resulting psychological and economic sequela. As countries start to ease lock-down measures and reopen industries, ensuring a healthy workplace for employees has become imperative. Thus, this paper presents a mobile app-based intelligent portable healthcare (pHealth) tool, called i WorkSafe, to assist industries in detecting possible suspects for COVID-19 infection among their employees who may need primary care. Developed mainly for low-end Android devices, the i WorkSafe app hosts a fuzzy neural network model that integrates data of employees' health status from the industry's database, proximity and contact tracing data from the mobile devices, and user-reported COVID-19 self-test data. Using the built-in Bluetooth low energy sensing technology and K Nearest Neighbor and K-means techniques, the app is capable of tracking users' proximity and trace contact with other employees. Additionally, it uses a logistic regression model to calculate the COVID-19 self-test score and a Bayesian Decision Tree model for checking real-time health condition from an intelligent e-health platform for further clinical attention of the employees. Rolled out in an apparel factory on 12 employees as a test case, the pHealth tool generates an alert to maintain social distancing among employees inside the industry. In addition, the app helps employees to estimate risk with possible COVID-19 infection based on the collected data and found that the score is effective in estimating personal health condition of the app user.