PubMed Health⌕ Search

PubMed · 12477639

Drug recognition expert evaluations made using limited data.

Abstract

The Drug Evaluation Classification (DEC) Program is used by Drug Recognition Expert (DRE) officers to determine whether a suspect is under the influence of a drug or drugs at the time of arrest, and, if so, what category of drug(s). The goal of this project was to investigate the relative importance of face-to-face interactions with the suspect, physical evidence (drugs or paraphernalia found), and confessions/statements made by the suspect (or others) in making these determinations. Seventy records of DRE evaluations were selected from a database containing information from all evaluations conducted in Oregon between 1996 and 1998. Each of the 70 records represented a suspect who had either taken a drug from one of four categories (CNS depressant, CNS stimulant, narcotic analgesic, or cannabis) or who had not taken a drug. To be included, the original DRE evaluation and the subsequent toxicology analysis had to agree that the suspect was under the influence of a drug from one of the four categories or not under the influence of a drug. Records from the 70 cases were submitted in written form to 18 Oregon DREs with statements made by suspects or arresting officers, confessions, toxicology results, and descriptions of drugs or paraphernalia found on the suspect omitted. Based only on the written reports of direct observations, and with physiological and psychophysical test results, the DREs attempted to determine whether each of the 70 suspects was under the influence of a drug or drugs, and, if so, what category of drug(s). If the officers determined that a suspect was under the influence of a drug, their accuracy in specifying the drug category was 81% for cannabis, 94% for narcotic analgesics, 78% for CNS stimulants, and 69% for CNS depressants. Overall accuracy in recognizing drug intoxication was 95%. These percentages indicate that although face-to-face interactions, physical evidence, and confessions/statements can be useful adjuncts to DRE decision-making, the majority of drug category decisions can be made solely on the basis of recorded suspect observations and DRE evaluation results.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

John A Smith, Charles E Hayes, Robert L Yolton, Dale A Rutledge, Karl Citek. 2002-12-04. Drug recognition expert evaluations made using limited data.. https://doi.org/10.1016/s0379-0738(02)00384-5

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗