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:

Pancreatic Cancer: A Survival Analysis Study in Oklahoma Pancreatic cancer is among the most deadly cancers. Risk factors associated with the disease include age, race, sex, smoking status, and diabetes status. We conducted a prospective analysis of risk factors and length of survival among pancreatic cancer patients living in Oklahoma between 1997 and 2012 (n=6,291). Kaplan-Meier survival curves were created followed by the log-rank test to compare difference in the survival time. Cox proportional hazard regression models were used to examine the strength of association through the estimated hazard ratios. The median survival time of the cohort was three months. Significant risk factors for reduced survival times included age, stage at diagnosis, and year of diagnosis. Results are in agreement with previous research findings. There have been small but noteworthy improvements in survival times for pancreatic cancer patients in Oklahoma. Length of survival during the study period was significantly associated with known risk factors such as age and stage of diagnosis.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": ["Pancreatic cancer", "deadly cancers", "Pancreatic Cancer", "disease"], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": [], "ClinicalAttribute": ["survival time", "survival times", "smoking status"], "Eukaryote": [], "Finding": ["Risk factors", "Results", "risk factors", "research findings"], "Food": [], "HealthCareActivity": [], "InjuryOrPoisoning": [], "IntellectualProduct": ["stage at diagnosis", "Cox proportional hazard regression models", "stage of diagnosis"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["race", "cohort"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["Study", "prospective analysis", "Survival Analysis", "study period"], "SpatialConcept": ["Oklahoma"], "Virus": []}