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 Input:
Paragraph: Background: BCoV is identified in both healthy and diarrheic calves, complicating its assessment as a primary pathogen. Objectives: To investigate the detection rates of bovine coronavirus (BCoV) in feces of healthy and diarrheic calves and to describe the usefulness of a pancoronavirus reverse transcriptase (RT) PCR (PanCoV-RT-PCR) assay to identify BCoV in samples of diarrheic calves. Animals: Two hundred and eighty-six calves <21 days. Calves with liquid or semiliquid feces, temperature >39.5°C, and inappetence were considered as cases, and those that had pasty or firm feces and normal physical examination were designated as controls. Methods: Prospective case-control study. A specific BCoV-RT-PCR assay was used to detect BCoV in fecal samples. Association between BCoV and health status was evaluated by exact and random effect logistic regression. Fecal (n = 28) and nasal (n = 8) samples from diarrheic calves were tested for the presence of BCoV by both the PanCoV-RT-PCR and a specific BCoV-RT-PCR assays. A Kappa coefficient test was used to assess the level of agreement of both assays. Results: BCoV was detected in 55% (157/286) of calves; 46% (66/143), and 64% (91/143) of healthy and diarrheic calves, respectively. Diarrheic calves had higher odds of BCoV presence than healthy calves (OR: 2.16, 95% CI: 1.26 to 3.83, P = 0.004). A good agreement between PanCoV-RT-PCR and BCoV-RT-PCR to detect BCoV was identified (j = 0.68, 95% CI: 0.392 to 0.967; P < 0.001). Conclusions and Clinical Importance: BCoV was more likely to be detected in diarrheic than healthy calves. The PanCoV-RT-PCR assay can be a useful tool to detect CoV samples from diarrheic calves. 
 Title: Detection of Bovine Coronavirus in Healthy and Diarrheic Dairy Calves

Ex Output:
True


Ex Input:
Paragraph: Purpose: COVID-19 has become global threaten. CT acts as an important method of 60 diagnosis. However, human-based interpretation of CT imaging is time consuming. 61 More than that, substantial inter-observer-variation cannot be ignored. We aim at 62 developing a diagnostic tool for artificial intelligence (AI)-based classification of CT 63 images for recognizing COVID-19 and other common infectious diseases of the lung. 64 Experimental Design: In this study, images were retrospectively collected and 65 prospectively analyzed using machine learning. CT scan images of the lung that show 66 or do not show COVID-19 were used to train and validate a classification framework 67 based on convolutional neural network. Five conditions including COVID-19 68 pneumonia, non-COVID-19 viral pneumonia, bacterial pneumonia, pulmonary 69 tuberculosis, and normal lung were evaluated. Training and validation set of images 70 were collected from Wuhan Jin Yin-Tan Hospital whereas test set of images were 71 collected from Zhongshan Hospital Xiamen University and the fifth Hospital of 72 Wuhan. 73 Results: Accuracy, sensitivity, and specificity of the AI framework were reported. For 74 test dataset, accuracies for recognizing normal lung, COVID-19 pneumonia, 75 non-COVID-19 viral pneumonia, bacterial pneumonia, and pulmonary tuberculosis 76 were 99.4%, 98.8%, 98.5%, 98.3%, and 98.6%, respectively. For the test dataset, 77 accuracy, sensitivity, specificity, PPV, and NPV of recognizing COVID-19 were 78 98.8%, 98.2%, 98.9%, 94.5%, and 99.7%, respectively. 79 All rights reserved. No reuse allowed without permission. the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 
 Title: Deep Learning-Based Recognizing COVID-19 Deep Learning-Based Recognizing COVID-19 and other Common Infectious Diseases of the Lung by Chest CT 2 Scan Images

Ex Output:
False


Ex Input:
Paragraph: One sentence summary: A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease. Editor: Edmundo Calva ABSTRACT A distinct difference between veterinary and human medicine is the routine use of antimicrobial mass medications (prophylaxis, metaphylaxis) to healthy individuals. The need for antimicrobial mass medications is based on beliefs that group/s of animals will contract a bacterial disease (i.e. morbidity) and/or die (i.e. mortality). Bovine respiratory disease (BRD) represents the major indication for cattle antimicrobials worldwide. The objectives were to perform a systematic review and meta-analysis of randomised controlled clinical trials (RCTs) for naturally occurring BRD investigating antimicrobial prophylaxis/metaphylaxis to prevent morbidity/mortality. In total, 58 publications met the inclusion criteria summarizing 169 individual RCTs, spanning 50 years . Antimicrobial prophylaxis and metaphylaxis demonstrated moderate, yet highly variable relative risk reductions in BRD morbidity. These were dependent on the antimicrobial classes used, dependent on metaphylaxis definition, BRD attack rates and duration of the RCTs. Best relative risk reductions were from broad-spectrum critically important antimicrobials, or combinations. BRD prophylaxis/metaphylaxis represents major antimicrobial consumption for highly variable short-term gains in absolute risk reduction of morbidity/mortality. Despite widespread use of prevention products, the need for antimicrobial mass medications should be re-evaluated since the underlying problem is more likely the segmented infrastructure of the feedlot and veal calf industries compared to the disease itself. 
 Title: Do antimicrobial mass medications work? A systematic review and meta-analysis of randomised clinical trials investigating antimicrobial prophylaxis or metaphylaxis against naturally occurring bovine respiratory disease

Ex Output:

False