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".

[EX Q]: Paragraph: Objective: The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. Methods: A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. Results: 76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR = 5.20, 95% CI 1.41-12.52, p = 0.010; 5 points), lymphocyte 0.8 * 10 9 /L (OR = 4.53, 95% CI 2.55-8.05, p < 0.001; 4 points), bacterial coinfection (OR = 3.71, 95% CI 2.11-6.51, p < 0.001; 4 points), acute-smoker (OR = 3.19, 95% CI 1.34-6.26, p = 0.001; 3 points), quit-smoker (OR = 2.18, 95% CI 0.99-4.82, p = 0.054; 2 points), hypertension (OR = 2.39, 95% CI 1.55-4.26, p = 0.003; 2 points) and age 60 years (OR = 2.14, 95% CI 1.04-4.39, p = 0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs. 0.717, p < 0.001). Conclusion: Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.
Title: Citation: Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score
[EX A]: False

[EX Q]: Paragraph: People in the Middle East have been affected by the Middle East Respiratory Syndrome CoronaVirus (MERS Co-V) since 2012. New cases are continuously reported especially in the Kingdom of Saudi Arabia, and the risk of exposure remains an issue. Data visualization plays a vital role in effective analysis of the data. In this paper, we introduce an interactive visualization application for MERS data collected from the Control and Command Centre, Ministry of Health website of Saudi Arabia. The data corresponding to the period from January 1, 2019 to February 28, 2019 was used in the present work. The attributes considered include gender, age, date of reporting, city, region, camel contact, description and status of the patient. The visualization tool has been developed using Shiny framework of R programming language. The application presents information in the form of interactive plots, maps and tables. The salient feature of the tool is that users can view and download data corresponding to the period of their choice. This tool can help decision makers in the detailed analysis of data and hence devise measures to prevent the spread of the disease.
Title: Shiny Framework Based Visualization and Analytics Tool for Middle East Respiratory Syndrome
[EX A]: False

[EX Q]: Paragraph: Introduction: The role of laboratory parameters in screening of COVID-19 cases has not been definitely established. This study aimed to evaluate the accuracy of laboratory parameters in predicting cases with positive RT-PCR for COVID-19. Methods: This diagnostic accuracy study was conducted on suspected COVID-19 patients, who presented to Behpooyan Clinic Medical center in Tehran (Iran) from 22 February to 14 March, 2020. Patients were divided into two groups based on the results of real time reverse transcriptase-polymerase chain reaction (RT-PCR) for COVID-19, and the accuracy of different laboratory parameters in predicting cases with positive RT-PCR was evaluated using area under the ROC curve (AUC). Results: Two hundred cases with the mean age of 41.3 14.6 (range: 19-78) years were studied (0.53% male). The result of RT-PCR for COVID-19 was positive in 70 (35%) cases. Patients with positive RT-PCR had significantly higher neutrophil (NEU) count (p = 0.0001), and C-reactive protein (CRP) (p = 0.04), lactate dehydrogenase (LDH) (p = 0.0001), aspartate aminotransferase (AST) (p = 0.001), alanine aminotransferase (ALT) (p = 0.0001), and Urea (p = 0.001) levels in serum. In addition, patients with positive RT-PCR had lower white blood cell (WBC) count (p = 0.0001) and serum albumin level (p = 0.0001) compared to others. ALT (AUC = 0.879), CRP (AUC = 0.870), NEU (AUC = 0.858), LDH (AUC = 0.835), and Urea (AUC = 0.835) had very good accuracy in predicting cases with positive RT-PCR for COVID-19, respectively. Conclusion: Our findings suggest that level of LDH, CRP, ALT and NEU can be used to predict the result of COVID-19 test. They can help in detection of COVID-19 patients.
Title: Parameters in Detection of COVID-19 Patients with Positive RT-PCR; a Diagnostic Accuracy Study
[EX A]:
False