ABSTRACT
Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.
ABSTRACT
The concept, approaches to learning (ATL), is an important dimension of children's school readiness, which reflects children's enthusiasm and engagement in learning settings. Due to coronavirus disease 2019 (COVID-19), when preschools were shut down for months and children's social interactions were greatly limited, preschoolers' ATL might be negatively affected. However, strengthened family-school connections might have the potential to reduce the adverse effects of the pandemic. Based on a sample of 340 preschoolers from 30 classes of 6 preschools in Wuhan, China, we explored the association between family-preschool connections and preschoolers' ATL scores. The findings showed that the preschoolers' ATL were statistically and positively associated with parental attendance at the online parent-teacher meetings initiated by the preschools (standardized coefficient = 0.15). Policy implications regarding the enhancement of family-preschool connections are discussed. Supplementary Information: The online version contains supplementary material available at 10.1007/s10643-021-01217-x.
ABSTRACT
The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.