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:

Risk Factors for Infection After Knee Arthroscopy: Analysis of 595,083 Cases From 3 United States Databases To identify and quantify patient- and procedure-related risk factors for post-arthroscopic knee infections using a large dataset. An administrative health care database including 8 years of records from 2 large commercial insurers and Medicare (a 5% random sample) was queried to identify all knee arthroscopies performed on patients aged at least 15 years using Current Procedural Terminology (CPT) codes. Each CPT code was designated as a high- or low-complexity procedure, with the former typically requiring accessory incisions or increased operative time. Deep infections were identified by a CPT code for incision and drainage within 90 days of surgery. Superficial infections were identified by International Classification of Diseases, Ninth Revision infection codes without any record of incision and drainage. Patients were compared based on age, sex, body mass index, tobacco use, presence of diabetes, and Charlson Comorbidity Index. A total of 526,537 patients underwent 595,083 arthroscopic knee procedures. Deep postoperative infections occurred at a rate of 0.22%. Superficial infections occurred at a rate of 0.29%. Tobacco use and morbid obesity were the largest risk factors for deep and superficial infections, respectively (P < .001; relative risk of 1.90 and 2.19, respectively). There were also higher infection rates among patients undergoing relatively high-complexity arthroscopies, men, obese patients, diabetic patients, and younger patients (in order of decreasing relative risk). Increased Charlson Comorbidity Index was associated with superficial and total infections (P < .001). Post-arthroscopic knee infections were more frequent among morbidly obese patients, tobacco users, patients undergoing relatively complex procedures, men, obese patients, diabetic patients, relatively young patients, and patients with increased comorbidity burdens in this study population. This knowledge may allow more informed preoperative counseling, aid surgeons in patient selection, and facilitate infection prevention by targeting individuals with higher inherent risk. Level IV, cross-sectional study.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["obese", "diabetic", "deep", "Infection", "superficial infections", "morbidly obese", "total infections", "infections", "infection rates", "postoperative infections", "morbid obesity", "Superficial infections", "superficial", "diabetes"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": ["body mass index"], "Eukaryote": [], "Finding": ["tobacco users", "patient-", "Risk Factors", "risk factors", "procedure-related risk factors"], "Food": [], "HealthCareActivity": ["low-complexity procedure", "drainage", "incisions", "incision", "preoperative counseling", "complex procedures", "high-complexity arthroscopies", "high-", "knee arthroscopies", "Knee Arthroscopy", "arthroscopic knee procedures"], "InjuryOrPoisoning": [], "IntellectualProduct": ["Current Procedural Terminology (CPT) codes", "CPT code", "administrative health care database", "record", "Databases", "dataset", "International Classification of Diseases, Ninth Revision infection codes"], "MedicalDevice": [], "Organization": ["insurers"], "PopulationGroup": ["men", "individuals"], "ProfessionalOrOccupationalGroup": ["aid surgeons"], "ResearchActivity": ["Analysis", "random sample", "cross-sectional study", "patient selection"], "SpatialConcept": ["United States"], "Virus": []}