TASK DEFINITION: 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".
PROBLEM: 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

SOLUTION: True

PROBLEM: Paragraph: Background: The goal of this study was to evaluate whether a live attenuated poliovirus vaccine (OPV) has clinically relevant interfering effect with non-polio infections causing otitis media in young children. Methods: Open trial in which the intervention group (64 children) received OPV at the age of 2, 3, 6 and 12 months. The control group (250 children) received IPV (inactivated polio vaccine) at the age of 6 and 12 months. Clinical symptoms were recorded by a questionnaire at the age of 3, 6, 12, 18 and 24 months. Results: Otitis media episodes were less frequent in the OPV than in the control group. A significant difference was seen at the age of 6-18 months (IRR = 0.76 [95% CI 0.59-0.94], P = 0.011) and was particularly clear among children, who attended daycare (IRR 0.37 [95% CI 0.19-0.71], P = 0.003). Conclusions: OPV provides some protection against otitis media. This effect may be mediated by viral interference with non-polio viruses.
Title: Viral interference induced by live attenuated virus vaccine (OPV) can prevent otitis media

SOLUTION: False

PROBLEM: Paragraph: Early warning systems for outbreaks of infectious diseases are an important application of the ecological theory of epidemics. A key variable predicted by early warning systems is the final outbreak size. However, for directly transmitted diseases, the stochastic contact process by which outbreaks develop entails fundamental limits to the precision with which the final size can be predicted. I studied how the expected final outbreak size and the coefficient of variation in the final size of outbreaks scale with control effectiveness and the rate of infectious contacts in the simple stochastic epidemic. As examples, I parameterized this model with data on observed ranges for the basic reproductive ratio (R 0 ) of nine directly transmitted diseases. I also present results from a new model, the simple stochastic epidemic with delayed-onset intervention, in which an initially supercritical outbreak (R 0 . 1) is brought under control after a delay. The coefficient of variation of final outbreak size in the subcritical case (R 0 , 1) will be greater than one for any outbreak in which the removal rate is less than approximately 2.41 times the rate of infectious contacts, implying that for many transmissible diseases precise forecasts of the final outbreak size will be unattainable. In the delayed-onset model, the coefficient of variation (CV) was generally large (CV . 1) and increased with the delay between the start of the epidemic and intervention, and with the average outbreak size. These results suggest that early warning systems for infectious diseases should not focus exclusively on predicting outbreak size but should consider other characteristics of outbreaks such as the timing of disease emergence.
Title: Limits to Forecasting Precision for Outbreaks of Directly Transmitted Diseases

SOLUTION:
True