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".

Input: Consider Input: Paragraph: Benchmarks and metrics related to laboratory test utilization are based on evidence-based medical literature that may suffer from a positive publication bias. Guidelines are only as good as the data reviewed to create them. Disruptive technologies require time for appropriate use to be established before utilization review will be meaningful. Metrics include monitoring the use of obsolete tests and the inappropriate use of lab tests. Test utilization by clients in a hospital outreach program can be used to monitor the impact of new clients on lab workload. A multi-disciplinary laboratory utilization committee is the most effective tool for modifying bad habits, and reviewing and approving new tests for the lab formulary or by sending them out to a reference lab.
Title: Establishing benchmarks and metrics for disruptive technologies, inappropriate and obsolete tests in the clinical laboratory

Output: False

Input: Consider Input: Paragraph: Extracts of Anthemis hyalina (Ah), Nigella sativa (Ns) and peels of Citrus sinensis (Cs) have been used as folk medicine to fight antimicrobial diseases. To evaluate the effect of extracts of Ah, Ns and Cs on the replication of coronavirus (CoV) and on the expression of TRP genes during coronavirus infection, HeLa-CEACAM1a (HeLa-epithelial carcinoembryonic antigen-related cell adhesion molecule 1a) cells were inoculated with MHV-A59 (mouse hepatitis virus-A59) at moi of 30. 1/50 dilution of the extracts was found to be the safe active dose. ELISA kits were used to detect the human IL-8 levels. Total RNA was isolated from the infected cells and cDNA was synthesized. Fluidigm Dynamic Array nanofluidic chip 96.96 was used to analyze the mRNA expression of 21 TRP genes and two control genes. Data was analyzed using the BioMark digital array software. Determinations of relative gene expression values were carried out by using the 2 -DDCt method (normalized threshold cycle (Ct) value of sample minus normalized Ct value of control). TCID 50 / ml (tissue culture infectious dose that will produce cytopathic effect in 50 % of the inoculated tissue culture cells) was found for treatments to determine the viral loads. The inflammatory cytokine IL-8 level was found to increase for both 24 and 48 h time points following Ns extract treatment. TRPA1, TRPC4, TRPM6, TRPM7, TRPM8 and TRPV4 were the genes which expression levels changed significantly after Ah, Ns or Cs extract treatments. The virus load decreased when any of the Ah, Ns or Cs extracts was added to the CoV infected cells with Ah extract treatment leading to undetectable virus load for both 6 and 8hpi. Although all the extract treatments had an effect on IL-8 secretion, TRP gene expression and virus load after CoV infection, it was the Ah extract treatment that showed the biggest difference in virus load. Therefore Ah extract is the best candidate in our hands that contains potential treatment molecule(s).
Title: The effects of Nigella sativa (Ns), Anthemis hyalina (Ah) and Citrus sinensis (Cs) extracts on the replication of coronavirus and the expression of TRP genes family

Output: False

Input: Consider 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
Output: True