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: 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: Pulmonary gene delivery is thought to play an important role in treating genetically related diseases and may induce immunity towards pathogens entering the body via the airways. In this study we prepared poly (D,L-lactide-co-glycolide) (PLGA) nanoparticles bearing polyethyleneimine (PEI) on their surface and characterized them for their potential in serving as non-viral gene carriers to the pulmonary epithelium. Particles that were synthesized at different PLGA-PEI ratios and loaded with DNA in several PEI -DNA ratios, exhibited narrow size distribution in all formulations, with mean particle sizes ranging between 207 and 231 nm. Zeta potential was strongly positive (above 30 mV) for all the PEI -DNA ratios examined and the loading efficiency exceeded 99% for all formulations. Internalization of the DNA-loaded PLGA -PEI nanoparticles was studied in the human airway submucosal epithelial cell line, Calu-3, and DNA was detected in the endo-lysosomal compartment 6 h after particles were applied. Cytotoxicity of these nanoparticles was dependent on the PEI -DNA ratio and best cell viability was achieved by PEI -DNA ratios 1:1 and 0.5:1. These findings demonstrate that PLGA-PEI nanoparticles are a potential new delivery system to carry genes to the lung epithelium. q 
 Title: PLGA -PEI nanoparticles for gene delivery to pulmonary epithelium

Ex Output:
False


Ex Input:
Paragraph: Background: Healthcare-associated infections (HCAIs) cause significant morbidity and mortality worldwide, and outbreaks are often only identified after they reach high levels. A wide range of data is collected within healthcare settings; however, the extent to which this information is used to understand HCAI dynamics has not been quantified. Aim: To examine the use of spatiotemporal analyses to identify and prevent HCAI transmission in healthcare settings, and to provide recommendations for expanding the use of these techniques. Methods: A systematic review of the literature was undertaken, focusing on spatiotemporal examination of infectious diseases in healthcare settings. Abstracts and full-text articles were reviewed independently by two authors to determine inclusion. Findings: In total, 146 studies met the inclusion criteria. There was considerable variation in the use of data, with surprisingly few studies (N ¼ 22) using spatiotemporal-specific analyses to extend knowledge of HCAI transmission dynamics. The remaining 124 studies were descriptive. A modest increase in the application of statistical analyses has occurred in recent years. Conclusion: The incorporation of spatiotemporal analysis has been limited in healthcare settings, with only 15% of studies including any such analysis. Analytical studies provided greater data on transmission dynamics and effective control interventions than studies without spatiotemporal analyses. This indicates the need for greater integration of spatiotemporal techniques into HCAI investigations, as even simple analyses provide significant improvements in the understanding of prevention over simple descriptive summaries. 
 Title: Spatial and temporal analyses to investigate infectious disease transmission within healthcare settings

Ex Output:

True