Plos One
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Early determination of the severity of Community-Acquired Pneumonia (CAP) is essential for better disease prognosis. Current predictors are suboptimal, and their clinical utility remains to be defined, highlighting the need for developing biomarkers with efficacious prognostic value. Sphingosine-1-phosphate (S1P) is a bioactive sphingolipid with a documented regulatory role in immune defense and maintenance of endothelial barrier integrity. ⋯ Based on multivariate logistic regression analysis, the plasma S1P concentrations showed significant predicting power for mortality (OR: 0.909; CI: 0.801-0.985; p < 0.05), intensive care unit admission (OR: 0.89; CI: 0.812-0.953; p < 0.005) and long hospital stay (OR: 0.978; CI: 0.961-0.992; p < 0.005). Interestingly, significantly elevated levels of S1P were noted in patients who received methylprednisolone treatment during hospitalization. These results suggest that S1P may be associated with the pathogenesis of CAP and may have prognostic utility in CAP and its therapy, especially in the Emergency Department setting.
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As part of the Models of Child Health Appraised (MOCHA) project, this study aimed to answer the following research questions: 1) How do European citizens perceive the quality of primary health care provided for children? And 2) What are their priorities with respect to quality assessment of primary health care aimed at satisfying children's needs? ⋯ Between countries, significant differences exist in the perceived quality of primary care and priorities with regard to quality assessment. Taking into account the citizens' perspective in decision-making means that aspects with low perceived quality that are highly prioritized warrant further action.
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Comparative Study
A comparative study on machine learning based algorithms for prediction of motorcycle crash severity.
Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. ⋯ Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.
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High quality evidence-based clinical practice guidelines (CPGs) have a major impact on the appropriate diagnosis and management and positive outcomes. The evidence-based healthcare for patients with attention deficit hyperactive disorder (ADHD) is challenging. The objective of this study was to appraise the quality of published CPGs for ADHD. ⋯ Reporting of CPG development is often poorly documented. Guideline development groups should aim to follow the AGREE II criteria to improve the standards and quality of CPGs. The NICE CPG showed the best quality. Embedding the AGREE II appraisal of CPGs in the training and education of healthcare providers is recommended. The protocol for this study was published in PROSPERO (International prospective register of systematic reviews). Link: http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42017078712 and is additionally available from protocols.io. Link: https://dx.doi.org/10.17504/protocols.io.q27dyhn.
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Domestic dogs are trained to a wide variety of roles including an increasing number of medical assistance tasks. Glycaemia alert dogs are reported to greatly improve the quality of life of owners living with Type 1 diabetes. Research into their value is currently sparse, on small numbers of dogs and provides conflicting results. In this study we assess the reliability of a large number of trained glycaemic alert dogs at responding to hypo- and hyper-glycaemic (referred to as out-of-range, OOR) episodes, and explore factors associated with variations in their performance. ⋯ The large sample shows that the individual performance of dogs is variable, but overall their sensitivity and specificity to OOR episodes are better than previous studies suggest. Results show that optimal performance of glycaemic alert dogs depends not only on good initial and ongoing training, but also careful selection of dogs for the conditions in which they will be working.