In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Ex Input:
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.

Ex Output:
Accuracy of using automated methods for detecting adverse events from electronic health record data: a research protocol


Ex Input:
Coronaviruses most often infect the respiratory or intestinal tract. Transmissible gastroenteritis virus (TGEV), a group 1 coronavirus, infects the porcine small intestine. Piglets up to the age of 3 weeks die from diarrhea caused by the viral gastroenteritis unless they are protected by antibodies. In addition to the cellular receptor, porcine aminopeptidase N, the TGEV spike protein binds to sialic acid residues. We have shown that the sialic acid binding activity mediates the binding of TGEV to a mucin-like glycoprotein present in porcine brush border membranes. This was shown by performing a virus overlay binding assay with proteins obtained from brush border membranes by lectin precipitation. Because of the reactivity with specific lectins we assume that the recognized glycoprotein has the characteristics of a mucin.

Ex Output:
Identification of Sugar Residues Involved in the Binding of TGEV to Porcine Brush Border Membranes


Ex Input:
In the last decade, advances in cancer therapy have led to improved survival in children and adolescents with malignant disorders. As cure rates improve, treatmentrelated toxicity, especially infections, accounts for a greater proportion of morbidity and mortality. Pediatric hematology and oncology patients are often highly susceptible to infection. Those with medical devices, such as indwelling central catheters, and those with intermittent or chronic neutropenia are particularly at high risk of healthcare-associated infections (HAIs) such as central line-associated bloodstream infections (CLABSI), Clostridium difficile infections (CDIs), ventilator-associated pneumonia (VAP), catheter-associated urinary tract infections (CAUTI), and respiratory viral infections. In the past, infectious complications of therapy for oncological and hematological disorders were regarded as largely unavoidable. It is now recognized that many, although not all, of the most common infections in this population are preventable. Collaborative quality improvement efforts have led to effective strategies to reduce rates of HAI and improved outcomes in these populations.

Ex Output:

Healthcare-Associated Infections in Pediatric Hematology-Oncology