TASK DEFINITION: In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".
PROBLEM: Paragraph: Background: Adverse events are associated with significant morbidity, mortality and cost in hospitalized patients. Measuring adverse events is necessary for quality improvement, but current detection methods are inaccurate, untimely and expensive. The advent of electronic health records and the development of automated methods for encoding and classifying electronic narrative data, such as natural language processing, offer an opportunity to identify potentially better methods. The objective of this study is to determine the accuracy of using automated methods for detecting three highly prevalent adverse events: a) hospital-acquired pneumonia, b) catheter-associated bloodstream infections, and c) in-hospital falls. Methods/design: This validation study will be conducted at two large Canadian academic health centres: the McGill University Health Centre (MUHC) and The Ottawa Hospital (TOH). The study population consists of all medical, surgical and intensive care unit patients admitted to these centres between 2008 and 2014. An automated detection algorithm will be developed and validated for each of the three adverse events using electronic data extracted from multiple clinical databases. A random sample of MUHC patients will be used to develop the automated detection algorithms (cohort 1, development set). The accuracy of these algorithms will be assessed using chart review as the reference standard. Then, receiver operating characteristic curves will be used to identify optimal cut points for each of the data sources. Multivariate logistic regression and the areas under curve (AUC) will be used to identify the optimal combination of data sources that maximize the accuracy of adverse event detection. The most accurate algorithms will then be validated on a second random sample of MUHC patients (cohort 1, validation set), and accuracy will be measured using chart review as the reference standard. The most accurate algorithms validated at the MUHC will then be applied to TOH data (cohort 2), and their accuracy will be assessed using a reference standard assessment of the medical chart. Discussion: There is a need for more accurate, timely and efficient measures of adverse events in acute care hospitals. This is a critical requirement for evaluating the effectiveness of preventive interventions and for tracking progress in patient safety through time. 
 Title: Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol

SOLUTION: True

PROBLEM: Paragraph: Only a month after the outbreak of pneumonia caused by 2019-nCoV, more than forty-thousand people were infected. This put enormous pressure on the Chinese government, medical healthcare provider, and the general public, but also made the international community deeply nervous. On the 25th day after the outbreak, the Chinese government implemented strict traffic restrictions on the area where the 2019-nCoV had originateddHubei province, whose capital city is Wuhan. Ten days later, the rate of increase of cases in Hubei showed a significant difference (p ¼ 0.0001) compared with the total rate of increase in other provinces of China. These preliminary data suggest the effectiveness of a traffic restriction policy for this pandemic thus far. At the same time, solid financial support and improved research ability, along with network communication technology, also greatly facilitated the application of epidemic prevention measures. These measures were motivated by the need to provide effective treatment of patients, and involved consultation with three major groups in policy formulationdpublic health experts, the government, and the general public. It was also aided by media and information technology, as well as international cooperation. This experience will provide China and other countries with valuable lessons for quickly coordinating and coping with future public health emergencies. 
 Title: Lessons learned from the 2019-nCoV epidemic on prevention of future infectious diseases

SOLUTION: False

PROBLEM: Paragraph: Ebolaviruses constitute a public health threat, particularly in Central and Western Africa. Host cell factors required for spread of ebolaviruses may serve as targets for antiviral intervention. Lectins, TAM receptor tyrosine kinases (Tyro3, Axl, Mer), T cell immunoglobulin and mucin domain (TIM) proteins, integrins, and Niemann-Pick C1 (NPC1) have been reported to promote entry of ebolaviruses into certain cellular systems. However, the factors used by ebolaviruses to invade macrophages, major viral targets, are poorly defined. Here, we show that mannose-specific lectins, TIM-1 and Axl augment entry into certain cell lines but do not contribute to Ebola virus (EBOV)-glycoprotein (GP)-driven transduction of macrophages. In contrast, expression of Mer, integrin αV, and NPC1 was required for efficient GP-mediated transduction and EBOV infection of macrophages. These results define cellular factors hijacked by EBOV for entry into macrophages and, considering that Mer and integrin αV promote phagocytosis of apoptotic cells, support the concept that EBOV relies on apoptotic mimicry to invade target cells. 
 Title: Analysis of Ebola Virus Entry Into Macrophages

SOLUTION:
False