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: 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
Example Output: True

Example Input: Paragraph: Improved vaccines and adjuvants are being developed to reduce the threat posed by a terrorist attack involving aerosolized anthrax spores. Nevertheless, uncertainty persists concerning the relative benefits of inducing mucosal vs systemic immunity to host survival following inhalational exposure to anthrax spores. This work examines the effect of delivering the licensed human vaccine (Anthrax Vaccine Adsorbed, AVA) combined with a CpG oligodeoxynucleotide (ODN) adjuvant intraperitoneally or intranasally to A/J mice. Results indicate that protection from inhalational anthrax correlates with the induction of a strong systemic rather than mucosal immune response, and demonstrate that protection is significantly improved and accelerated by the addition of CpG ODN. 
 Title: Systemic but not mucosal immunity induced by AVA prevents inhalational anthrax
Example Output: True

Example Input: Paragraph: Like most viruses that replicate in the cytoplasm, mammalian reoviruses assemble membranous neo-organelles called inclusions that serve as sites of viral genome replication and particle morphogenesis. Viral inclusion formation is essential for viral infection, but how these organelles form is not well understood. We investigated the biogenesis of reovirus inclusions. Correlative light and electron microscopy showed that endoplasmic reticulum (ER) membranes are in contact with nascent inclusions, which form by collections of membranous tubules and vesicles as revealed by electron tomography. ER markers and newly synthesized viral RNA are detected in inclusion internal membranes. Live-cell imaging showed that early in infection, the ER is transformed into thin cisternae that fragment into small tubules and vesicles. We discovered that ER tubulation and vesiculation are mediated by the reovirus NS and NS proteins, respectively. Our results enhance an understanding of how viruses remodel cellular compartments to build functional replication organelles. IMPORTANCE Viruses modify cellular structures to build replication organelles. These organelles serve as sites of viral genome replication and particle morphogenesis and are essential for viral infection. However, how these organelles are constructed is not well understood. We found that the replication organelles of mammalian reoviruses are formed by collections of membranous tubules and vesicles derived from extensive remodeling of the peripheral endoplasmic reticulum (ER). We also observed that ER tubulation and vesiculation are triggered by the reovirus NS and NS proteins, respectively. Our results enhance an understanding of how viruses remodel cellular compartments to build functional replication organelles and provide functions for two enigmatic reovirus replication proteins. Most importantly, this research uncovers a new mechanism by which viruses form factories for particle assembly. 
 Title: Reovirus NS and NS Proteins Remodel the Endoplasmic Reticulum to Build Replication Neo-Organelles
Example Output:
True