You will be shown an abstract from a biomedical research paper. Given this abstract, your task is to extract all unique entities of the following types: ["HealthCareActivity", "InjuryOrPoisoning", "BodySubstance", "IntellectualProduct", "AnatomicalStructure", "SpatialConcept", "Chemical", "Bacterium", "MedicalDevice", "Organization", "BiomedicalOccupationOrDiscipline", "Finding", "BiologicFunction", "Virus", "ResearchActivity", "ClinicalAttribute", "PopulationGroup", "Eukaryote", "BodySystem", "Food", "ProfessionalOrOccupationalGroup"].

Please return the output as a JSON object of the format: {"Virus": ["HIV", ...], "Bacterium": ["MRSA", ...], "AnatomicalStructure": ["Lung", ...], "BodySystem": ["CNS", ...], "BodySubstance": ["Serum", ...], "Finding": ["Headache", ...], "InjuryOrPoisoning": ["Fracture", ...], "BiologicFunction": ["Death", ...], "HealthCareActivity": ["Biopsy", ...], "ResearchActivity": ["Clinical trial", ...], "MedicalDevice": ["Lenses", ...], "SpatialConcept": ["Camps", ...], "BiomedicalOccupationOrDiscipline": ["Forensic medicine", ...], "Organization": ["WHO", ...], "ProfessionalOrOccupationalGroup": ["Provider", ...], "PopulationGroup": ["Swimmers", ...], "Chemical": ["Gold", ...], "Food": ["Rice", ...], "IntellectualProduct": ["RPAM", ...], "ClinicalAttribute": ["Biomarker", ...], "Eukaryote": ["Dogs", ...]}. The keys should be entity types and values should be lists of extracted entities belonging to the corresponding type. If you cannot find entities belonging to a specific type, the value should be [].

Only output the JSON object and do not include any additional text.

Abstract:

Management of skin and soft-tissue infections at a community teaching hospital using a severity-of-illness tool Skin and soft-tissue infections (SSTIs) encompass a diverse range of infections of varying severity. The Clinical Resource Efficiency Support Team (CREST) scoring system stratifies patients into four classes (I = least severe to IV = most severe) based on the Standardized Early Warning Score (SEWS). The objective of this study was to apply CREST to hospitalized patients with SSTIs in order to quantify disease severity and evaluate appropriateness of antibiotic management. This was a retrospective, hypothesis -generating, single-centre evaluation of hospitalized patients with SSTIs admitted in 2011. Based on CREST classification, the empirical antimicrobial choices were categorized as appropriate, over-treatment or under-treatment. A total of 369 patients were screened and 200 met the inclusion criteria. The majority of patients were classified as either CREST class I (n = 68) or class I I (n = 102). Over-treatment was more common in the less severe classes (88% and 32% in class I and class II, respectively; P < 0.05). Sixty-three percent of class I (n = 43) were over-treated due to both the use of intravenous antibiotic s when oral therapy was sufficient and use of unnecessarily broad-spectrum antibiotics. In contrast, 25% (n = 26) of class II were over-treated due to use of unnecessarily broad-spectrum antibiotics. Overall clinical failure rates remained low with only 1%, 4% and 17% of patients unable to achieve initial response in class II, class III and class IV. Retrospective application of CREST identified opportunities to improve the management of SSTIs. CREST can be of great value in discriminating less- severe SSTIs, which can be treated on an outpatient basis.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["Skin and soft-tissue infections", "SSTIs", "infections", "skin and soft-tissue infections"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["antibiotic", "antimicrobial", "broad-spectrum antibiotics"], "ClinicalAttribute": ["response"], "Eukaryote": [], "Finding": ["clinical failure"], "Food": [], "HealthCareActivity": ["under-treatment", "evaluation", "over-treatment", "Over-treatment", "oral therapy", "management", "treated", "Management", "admitted", "application", "over-treated", "evaluate"], "InjuryOrPoisoning": [], "IntellectualProduct": ["classified", "Clinical Resource Efficiency Support Team (CREST) scoring system", "classes", "stratifies", "CREST", "SEWS", "Standardized Early Warning Score"], "MedicalDevice": [], "Organization": ["teaching hospital"], "PopulationGroup": [], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["retrospective", "study"], "SpatialConcept": [], "Virus": []}