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: Insults to the alveoli usually lead to inefficient gas exchange or even respiratory failure, which is difficult to model in animal studies. Over the past decade, stem cell-derived self-organizing three-dimensional organoids have emerged as a new avenue to recapitulate respiratory diseases in a dish. Alveolar organoids have improved our understanding of the mechanisms underlying tissue homeostasis and pathological alterations in alveoli. From this perspective, we review the state-of-the-art technology on establishing alveolar organoids from endogenous lung epithelial stem/progenitor cells or pluripotent stem cells, as well as the use of alveolar organoids for the study of respiratory diseases, including idiopathic pulmonary fibrosis, tuberculosis infection, and respiratory virus infection. We also discuss challenges that need to be overcome for future application of alveolar organoids in individualized medicine. 
 Title: Organoids as a Powerful Model for Respiratory Diseases

Ex Output:
True


Ex Input:
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

Ex Output:
True


Ex Input:
Paragraph: Vaccination is the most effective strategy for prevention and control of influenza. Timely production and deployment of seasonal influenza vaccines is based on an understanding of the epidemiology of influenza and on global disease and virologic surveillance. Experience with seasonal influenza vaccines guided the initial development of pandemic influenza vaccines. A large investment in pandemic influenza vaccines in the last decade has resulted in much progress and a body of information that can now be applied to refine the established paradigm. Critical and complementary considerations for pandemic influenza vaccines include improved assessment of the pandemic potential of animal influenza viruses, proactive development and deployment of pandemic influenza vaccines, and application of novel platforms and strategies for vaccine production and administration. 
 Title: Refining the approach to vaccines against influenza A viruses with pandemic potential

Ex Output:

False