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:

Locating the Seventh Cervical Spinous Process: Development and Validation of a Multivariate Model Using Palpation and Personal Information The aim of this study was to develop and validate a multivariate prediction model, guided by palpation and personal information, for locating the seventh cervical spinous process (C7SP). A single-blinded, cross-sectional study at a primary to tertiary health care center was conducted for model development and temporal validation. One-hundred sixty participants were prospectively included for model development (n = 80) and time-split validation stages (n = 80). The C7SP was located using the thorax-rib static method (TRSM). Participants underwent chest radiography for assessment of the inner body structure located with TRSM and using radio-opaque markers placed over the skin. Age, sex, height, body mass, body mass index, and vertex-marker distanc e (DV-M) were used to predict the distance from the C7SP to the vertex (DV-C7). Multivariate linear regression modeling, limits of agreement plot, histogram of residues, receiver operating characteristic curves, and confusion tables were analyzed. The multivariate linear prediction model for DV-C7 (in centimeters) was DV-C7 = 0.986DV-M + 0.018(mass) + 0.014(age) - 1.008. Receiver operating characteristic curves had better discrimination of DV-C7 (area under the curve = 0.661; 95% confidence interval = 0.541-0.782; P = .015) than DV-M (area under the curve = 0.480; 95% confidence interval = 0.345-0.614; P = .761), with respective cutoff points at 23.40 cm (sensitivity = 41%, specificity = 63%) and 24.75 cm (sensitivity = 69%, specificity = 52%). The C7SP was correctly located more often when using predicted DV-C7 in the validation sample than when using the TRSM in the development sample: n = 53 (66%) vs n = 32 (40%), P < .001. Better accuracy was obtained when locating the C7SP by use of a multivariate model that incorporates palpation and personal information.
{"AnatomicalStructure": ["Seventh Cervical Spinous Process", "seventh cervical spinous process", "C7SP", "body structure"], "Bacterium": [], "BiologicFunction": [], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": ["skin"], "Chemical": [], "ClinicalAttribute": ["body mass index"], "Eukaryote": [], "Finding": [], "Food": [], "HealthCareActivity": ["thorax-rib static method", "chest radiography", "TRSM", "palpation", "Palpation"], "InjuryOrPoisoning": [], "IntellectualProduct": ["Multivariate linear regression modeling", "multivariate prediction model", "confusion tables", "Multivariate Model", "multivariate model", "multivariate linear prediction model"], "MedicalDevice": ["radio-opaque markers"], "Organization": ["primary to tertiary health care center"], "PopulationGroup": ["participants", "Participants"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["temporal validation", "single-blinded, cross-sectional study"], "SpatialConcept": ["inner", "vertex"], "Virus": []}