Simplifying the Process for Identifying Drug Combinations by Drug Recognition Experts
| Autor: | Douglas James Beirness, Amy J. Porath-Waller |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Narcotics
Drug Automobile Driving Canada medicine.medical_specialty genetic structures Substance-Related Disorders media_common.quotation_subject Poison control Audiology Predictive Value of Tests Injury prevention Humans Medicine Simulation Cannabis media_common Multinomial logistic regression Analysis of Variance Ethanol biology business.industry Public Health Environmental and Occupational Health Human factors and ergonomics biology.organism_classification Substance Abuse Detection Drug Combinations Logistic Models Predictive value of tests Central Nervous System Stimulants Analysis of variance business Safety Research |
| Zdroj: | Traffic Injury Prevention. 11:453-459 |
| ISSN: | 1538-957X 1538-9588 |
| DOI: | 10.1080/15389588.2010.489199 |
| Popis: | The purpose of this study is to statistically identify the set of drug-related cues from Drug Evaluation and Classification (DEC) evaluations that significantly predict the categories of drugs used by suspected drug-impaired drivers.Data from 819 completed Canadian DEC evaluations of combinations of central nervous system (CNS) stimulants with cannabis, CNS stimulants with narcotic analgesics, cannabis with alcohol, and no-drug cases were analyzed using a multinomial logistic regression procedure.Eleven clinical indicators from the DEC evaluations significantly enhanced the prediction of drugs used by suspected drug-impaired drivers, including condition of the eyes, lack of convergence, rebound dilation, reaction to light, mean pulse rate, presence of visible injection sites, performance on the Horizontal Gaze Nystagmus Test, pupil size in darkness, performance on the One-Leg Stand Test, muscle tone, and performance on the Walk-and-Turn Test.The findings from this study will facilitate the process of identifying the correct categories of drugs ingested by suspected drug-impaired drivers by focusing on critical signs and symptoms of drug influence. This work will have direct and immediate relevance to the training of drug recognition experts (DREs) by providing the foundation for an innovative, statistically based approach to drug classification decisions by DREs. This research will also facilitate the enforcement of drug-impaired driving laws in Canada to help make Canadian roadways safer for all. |
| Databáze: | OpenAIRE |
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