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".

Example Input: Paragraph: medRxiv preprint [Abstract]Objective Coronavirus disease 2019 (COVID-19) has become pandemic in the world. The need for IgG-IgM combined antibody test is booming, but data on diagnostic indexes evaluation was inadequate. The aim of this study was to evaluate diagnostic indexes of a rapid IgG-IgM combined antibody test for SARS-CoV-2. Methods A total of 179 patients were enrolled. Serum were collected for IgG-IgM combined antibody test and corresponding nasal and pharyngeal swab specimens were collected for SARS-CoV-2 RT-PCR. According to SARS-CoV-2 RT-PCR results, patients under study were categorized as PCR positive group in 90 patients and PCR negative group in 89 patients. Results 1. Of the 90 PCR positive samples, 77 were tested positive by SARS-CoV-2 IgG-IgM test kit, yielding a sensitivity of 85.6%. Meanwhile, of the 89 PCR negative sample, 8 samples were detected positive, resulting in a specificity of 91%. Positive predictive value, negative predictive value and accuracy of this test kit was 95.1%, 82.7%, and 88.3%, respectively. Kappa efficiency between IgG/IgM test kit and RT-PCR were 0.75. 2. Accuracy in mild/common and severe/critical subgroup were 73.9% and 97.7%, respectively. Accuracy in clinical confirmed, suspected cases and other disease subgroups were 70%, 60%, and 100%, respectively. 3. Patients were further divided into '0 -7', '8 -15' and '>= 16' groups according to the time from illness onset to sample collection. Sensitivity, specificity and accuracy in these three groups were 18.8%, 77.8% and 40%; 100%, 50% and 87.5%; 100%, 64.3%, and 93.9, respectively. Conclusion The sensitivity and specificity of this ease-of-use IgG/IgM combined test kit were adequate, plus short turnaround time, no specific requirements for additional equipment or skilled technicians, all of these collectively contributed to its competence for mass testing. At the current stage, it cannot take the place of SARA-CoV-2 nucleic acid RT-PCR, but can be served as a complementary option for RT-PCR. The combination of RT-PCR and IgG-IgM combined test kit could provide further insight into SARS-CoV-2 infection diagnosis.
Title: Diagnostic Indexes of a Rapid IgG/IgM Combined Antibody Test for SARS-CoV-2
Example Output: True

Example 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
Example Output: False

Example Input: Paragraph: Recent results have provided increasing evidence for the existence of an intermediate membrane compartment between the rough endoplasmic reticulum and the Golgi complex which seems to function in protein sorting and the regulation of membrane traffic in the early part of the exocytic pathway . Localization of resident marker proteins has shown that this compartment consists of both peripheral and central elements . The aim of the present review is to combine the data on the pre-Golgi compartment with previous ideas of membrane traffic at the ER-Golgi interface . We propose a model which describes how mobile, endosome-like elements of the pre-Golgi compartment function in the generation of the compositional andfunctional boundary between the widely distributed ER and the more central4 located Golgi stacks .
Title: Pathways of protein sorting and membrane traffic between the rough endoplasmic reticulum and the Golgi complex jaakko Saraste *t and Esa Kuismanent
Example Output:
True