instruction:
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".
question:
Paragraph: The recent coronavirus pandemic follows in its early stages an almost exponential expansion, with the number of cases as a function of time reasonably well fit by N (t) e t , in many countries. We analyze the rate in different countries, choosing as a starting point in each country the first day with 30 cases and fitting for the following 12 days, capturing thus the early exponential growth in a rather homogeneous way. We look for a link between the rate and the average temperature T of each country, in the month of the epidemic growth. We analyze a base set of 42 countries, which developed the epidemic earlier, and an extended set of 88 countries, which developed the epidemic more recently. Fitting with a linear behavior (T ), we find evidence in both datasets for a decreasing growth rate as a function of T , at 99.66%C.L. and 99.86%C.L. in the base and extended dataset, respectively. In the base set, going beyond a linear model, a peak at about (7.7 3.6) C seems to be present in the data. Our findings give hope that, for northern hemisphere countries, the growth rate should significantly decrease as a result of both warmer weather and lockdown policies. In general the propagation should be hopefully stopped by strong lockdown, testing and tracking policies, before the arrival of the next cold season. I.
Title: Temperature dependence of COVID-19 transmission
answer:
True

question:
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
answer:
True

question:
Paragraph: The exponential growth in database of bio-molecular sequences have spawned many approaches towards storage, retrieval, classification and analyses requirements. Alignment-free techniques such as graphical representations and numerical characterisation (GRANCH) methods have enabled some detailed analyses of large sequences and found a number of different applications in the eukaryotic and prokaryotic domain. In particular, recalling the history of pandemic influenza in brief, we have followed the progress of viral infections such as bird flu of 1997 onwards and determined that the virus can spread conserved over space and time, that influenza virus can undergo fairly conspicuous recombination-like events in segmented genes, that certain segments of the neuraminidase and hemagglutinin surface proteins remain conserved and can be targeted for peptide vaccines. We recount in some detail a few of the representative GRANCH techniques to provide a glimpse of how these methods are used in formulating quantitative sequence descriptors to analyse DNA, RNA and protein sequences to derive meaningful results. Finally, we survey the surveillance techniques with a special reference to how the GRANCH techniques can be used for the purpose and recount the forecasts made of possible metamorphosis of pandemic bird flu to pandemic human infecting agents.
Title: Alignment-Free Analyses of Nucleic Acid Sequences Using Graphical Representation (with Special Reference to Pandemic Bird Flu and Swine Flu)
answer:
False