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:

Prevalence of Self-Reported Prescription Drug Use in a National Sample of U.S. Drivers Drug -involved driving has become an increasing concern. Although the focus has been on illegal drugs, there is evidence that prescribed medications can impair driving ability. The purpose of this study was to determine the self-reported prevalence of prescription drug use, including medical and nonmedical use, among a nationally representative sample of drivers and to report related driver characteristics. As part of the 2013-2014 National Roadside Survey, drivers from 60 sites were randomly recruited and asked to complete a survey on prescription drug use. Almost 20% of drivers reported using a prescription drug within the past 2 days, with the most common drug class being sedatives (8.0%), followed by antidepressants (7.7%), narcotics (7.5%), and stimulants (3.9%). Drivers who reported prescription drug use were significantly more likely to be female, older, non-Hispanic White, and report disability. Three of four drivers who reported medication use (78.2%) said the drug was prescribed for their use; the odds of using without a prescription were significantly higher for males, Black/African American, and Hispanic drivers, and lower for older drivers. Among those with a prescription, taking more than prescribed was most common for narcotics (6.8%), followed by sedatives (4.8%), stimulants (3.8%), and antidepressants (1.5%). These findings help to identify drivers using potentially impairing prescription drugs, both medically and nonmedically, and may inform the targeting of interventions to reduce impaired driving related to medications.
{"AnatomicalStructure": [], "Bacterium": [], "BiologicFunction": [], "BiomedicalOccupationOrDiscipline": [], "BodySubstance": [], "BodySystem": [], "Chemical": ["prescribed medications", "prescription drug", "Drug", "stimulants", "sedatives", "antidepressants", "illegal drugs", "medication", "drug", "prescription drugs", "narcotics", "common drug class", "Prescription Drug"], "ClinicalAttribute": [], "Eukaryote": [], "Finding": ["disability", "impair driving ability", "nonmedical", "nonmedically", "impaired driving"], "Food": [], "HealthCareActivity": ["determine", "interventions"], "InjuryOrPoisoning": [], "IntellectualProduct": ["prescription", "medications"], "MedicalDevice": [], "Organization": [], "PopulationGroup": ["drivers", "Hispanic", "Drivers", "Black/African American", "older", "driver"], "ProfessionalOrOccupationalGroup": [], "ResearchActivity": ["self-reported", "National Roadside Survey", "Self-Reported"], "SpatialConcept": ["U.S"], "Virus": []}