Objective: Although there is a considerable amount of data in the literature regarding the association between alcohol consumption and injuries treated in emergency rooms, little is known about the relationship between such injury and the use of other substances. The objective of this study was to estimate the prevalence of substance use in patients admitted to the emergency room for non-fatal injuries.
Method: A prospective cross-sectional study assessing all patients admitted to the emergency room within 6 hours after a non-fatal injury was conducted over a three-month period. The following were used as measures of alcohol and drug use: a standardized World Health Organization questionnaire; a self-administered questionnaire related to drug consumption within the 24 hours preceding contact; the Drug Abuse Screening Test; urine screens for cannabis, cocaine and benzodiazepines; and determination of blood alcohol concentration. Descriptive analyses were performed and the confidence interval used was 95%.
Results: A total of 353 patients were included. Cannabis and cocaine screens were conducted for 242 patients and benzodiazepine screens were conducted for 166. Blood alcohol concentrations reached the level of positivity in 11% (n = 39), and 10% (n = 33) presented some degree of intoxication. Among the 242 patients screened, 13.6% (n = 33) tested positive for cannabis, and 3.3% (n = 8) tested positive for cocaine, whereas 4.2% (n = 7) of the 166 patients screened tested positive for benzodiazepines.
Conclusions: Substance use was highly prevalent among these individuals. In this sample, the frequency for the use of cannabis (an illicit drug) was comparable to that of alcohol. More studies are needed in order to characterize such use among Brazilians and to develop proper approaches to such cases, with the aim of reducing substance use and its consequences.
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http://dx.doi.org/10.1590/s1516-44462006000300009 | DOI Listing |
Drugs Real World Outcomes
January 2025
Takeda Pharmaceuticals USA, Inc., 45 Hayden Avenue, Lexington, MA, 02421, USA.
Background: Lanadelumab is the only long-term prophylaxis indicated for reduced administration frequency in patients with hereditary angioedema who have been well controlled for > 6 months. Understanding the characteristics of patients who reduce administration frequency will help identify populations where frequency modifications may be appropriate.
Objective: We aimed to describe characteristics of patients who did and did not reduce lanadelumab administration frequency to inform real-world dosing regimens, and characteristics indicative of sustained frequency reduction.
J Gen Intern Med
January 2025
Icahn School of Medicine at Mount Sinai, Institute for Health Equity Research, New York, USA.
Background: Over 60 million patients in the USA have limited English proficiency (LEP) and experience barriers in care. Still, there exists no standardized method of monitoring the utilization of language interpreting services (LIS).
Objective: To introduce a methodological approach to systematically monitor utilization of LIS for LEP patients.
Sci Rep
January 2025
Infectious Diseases Clinic, Azienda Sanitaria Universitaria Friuli Centrale, 33100, Udine, Italy.
Enterococcus faecalis is responsible for numerous serious infections, and treatment options often include ampicillin combined with an aminoglycoside or dual beta-lactam therapy with ampicillin and a third-generation cephalosporin. The mechanism of dual beta-lactam therapy relies on the saturation of penicillin-binding proteins (PBPs). Ceftobiprole exhibits high affinity binding to nearly all E.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Urology, Vanderbilt University Medical Center, Nashville, USA.
Recent advancements of large language models (LLMs) like generative pre-trained transformer 4 (GPT-4) have generated significant interest among the scientific community. Yet, the potential of these models to be utilized in clinical settings remains largely unexplored. In this study, we investigated the abilities of multiple LLMs and traditional machine learning models to analyze emergency department (ED) reports and determine if the corresponding visits were due to symptomatic kidney stones.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Emergency Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
This study developed a predictive model using deep learning (DL) and natural language processing (NLP) to identify emergency cases in pediatric emergency departments. It analyzed 87,759 pediatric cases from a South Korean tertiary hospital (2012-2021) using electronic medical records. Various NLP models, including four machine learning (ML) models with Term Frequency-Inverse Document Frequency (TF-IDF) and two DL models based on the KM-BERT framework, were trained to differentiate emergency cases using clinician transcripts.
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