BackgroundThere is controversy surrounding the management of young children who have a fever without a source (FWS). Several strategies have been designed with the purpose of managing children with FWS.AimsTo assess the applicability of a standardized guideline for children up to 36 months of age with FWS.SettingPediatric emergency unit, Al-Adan Hospital, Kuwait City, Kuwait, from May 2011 to October 2011.DesignProspective, cross-sectional study.Methods and materialsThe study involved children with FWS up to 36 months of age. The guideline classifies the risk of serious bacterial infection (SBI) according to the age of the child, the presence or absence of toxemia, clinical presentation, and laboratory screening tests.ResultsA total of 481 children were included in the present study, but only 385 cases completed the study; 3.9% of patients had toxemia at the initial evaluation. We found 26 children with SBI (6.8%); 12 patients with SBI did not present with toxemia. In all, 40.4% of studied newborns were diagnosed as having a urinary tract infection, and 42.7% of patients as self-limited probable viral etiology. Of the 109 young infants without toxemia, 53.2% were classified as being at high risk of SBI. Of the 163 toddlers without toxemia, 72.4% were treated with antibiotics; 48.4% of patients received therapeutic treatment and 25.8% received empirical treatment.ConclusionThe guideline followed in our pediatric emergency unit seemed to be appropriate in following up with these children using simple laboratory tests. The most frequent SBI in this sample was urinary tract infection.
A Rumor is considered as unverified pieces of information circulating, that arise in the context of uncertainty, with negative impact, and falsely attributes. Unfortunately, terribly damaging form of communication are the results of rumors. Rumors spread on social media with no exception, and only serve to amplify the negative effects on people and businesses. This paper aims to present literature related to rumor detection on social network and try to find a link on how human behavior is affected by it. Therefore, it surveys the rumors detection frameworks, algorithms, and computational techniques that help in detecting and blocking rumors from spreading on social media. Also, attributes that may identify and describe a rumor and human behavior towards rumors are gathered, unified, and arranged in an integrated recommended list. This list of attributes may be the guide for detecting and capturing rumors with their changeable inconstant form. As a result, from this trial a proposed framework is presented to offer an idea for dealing with human behavior on rumors. This model presents open issues and forwarded ideas to provide an insight for future work in the area of building Rumor-Human Behavior computational models.
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