National Surveillance of Injury in the Republic of Korea: Increased Injury Vulnerability in the Late Middle Age

Using Large Language Models to Extract Core Injury Information From Emergency Department Notes

BACKGROUND

Injuries pose a significant global health challenge due to their high incidence and mortality rates. Although injury surveillance is essential for prevention, it is resource-intensive. This study aimed to develop and validate locally deployable large language models (LLMs) to extract core injury-related information from Emergency Department (ED) clinical notes.

METHODS

We conducted a diagnostic study using retrospectively collected data from January 2014 to December 2020 from two urban academic tertiary hospitals. One served as the derivation cohort and the other as the external test cohort. Adult patients presenting to the ED with injury-related complaints were included. Primary outcomes included classification accuracies for information extraction tasks related to injury mechanism, place of occurrence, activity, intent, and severity. We fine-tuned a single generalizable Llama-2 model and five distinct Bidirectional Encoder Representations from Transformers (BERT) models for each task to extract information from initial ED physician notes. The Llama-2 model was able to perform different tasks by modifying the instruction prompt. Data recorded in injury registries provided the gold standard labels. Model performance was assessed using accuracy and macro-average F1 scores.

RESULTS

The derivation and external test cohorts comprised 36,346 and 32,232 patients, respectively. In the derivation cohort's test set, the Llama-2 model achieved accuracies (95% confidence intervals) of 0.899 (0.889-0.909) for injury mechanism, 0.774 (0.760-0.789) for place of occurrence, 0.679 (0.665-0.694) for activity, 0.972 (0.967-0.977) for intent, and 0.935 (0.926-0.943) for severity. The Llama-2 model outperformed the BERT models in accuracy and macro-average F1 scores across all tasks in both cohorts. Imposing constraints on the Llama-2 model to avoid uncertain predictions further improved its accuracy.

CONCLUSION

Locally deployable LLMs, trained to extract core injury-related information from free-text ED clinical notes, demonstrated good performance. Generative LLMs can serve as versatile solutions for various injury-related information extraction tasks.

Phytochemical Composition and Antimicrobial Efficacy of Salvadora persica Root Extracts Against Carbapenem-Resistant Acinetobacter baumannii

Background Carbapenem-resistant Acinetobacter baumannii (CRAB) are difficult to eradicate from the environment and are virtually immune to all antibiotics. Consequently, CRAB may culminate in severe outbreaks and fatal infections among people attending hospitals and nursing homes. Salvadora persica has been used as an herbal remedy and chewing sticks for dental cleansing. Evaluating S. persica's efficacy against CRAB may provide an alternative approach to treating CRAB infections in healthcare environments, considering its traditional application in dental hygiene. Employing S. persica as an herbal remedy could be a part of a more sustainable approach to control CRAB infections. Aim To investigate the phytochemical composition of S. persica and evaluate its antimicrobial properties. Materials and methods The roots were extracted by Soxhlet apparatus using n-hexane, chloroform, and methanol. Each extract was analyzed using gas chromatography-mass spectrometry (GCMS) and characterized using WN908.L and National Institute of Standards and Technology (NIST) libraries. The antimicrobial activity of each extract against CRAB was evaluated using a broth microdilution assay to determine the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC). Results The GCMS analysis of different solvent extracts of S. persica roots showed the presence of various phytochemical compounds such as steroids, phenolic compounds, fatty acids, alcohols, terpenoids, and vitamin E. Both chloroform and hexane extracts showed the most effective antimicrobial activity with a MIC value of 3.13 mg/mL and an MBC value of 12.50 mg/mL, respectively. Benzoic acid was the major phytochemical compound identified from S. persica extract. N-hexane, chloroform, and methanol extracts exhibited maximum antimicrobial activity due to the presence of active compounds in them. Conclusion Chloroform and hexane extracts showed the most potent antibacterial activities against CRAB.

Healthcare Utilization for Lateral Epicondylitis: A 9-Year Analysis of the 2010–2018 Health Insurance Review and Assessment Service National Patient Sample Data

This retrospective cross-sectional study examined healthcare utilization among 213,025 patients with lateral epicondylitis over a nine-year period using the 2010–2018 Health Insurance Review and Assessment Service (HIRA) data (ICD code M771). Healthcare utilization, types of treatment, and the route of the visit were analyzed with frequency analysis for Western medicine (WM) and Korean medicine (KM). The findings revealed that the number of patients visiting WM and KM facilities for lateral epicondylitis rose every year from 2010 to 2018. Over this period, the age distribution of patients was 45–54 years (39.93%), 55–64 (23.12%), and 35–44 years (21.07%), and there were slightly more female patients (53.66%) than male patients (46.34%). The number of claims for lateral epicondylitis tended to increase with decreasing average monthly temperature; an increased proportion of middle-aged patients (45–64 years) was the most evident. The most frequently performed interventions in WM were subcutaneous or intramuscular injection (injection), deep heat therapy (physical therapy), and spinal peripheral nerve block-axillary nerve block (treatment/operation); the most frequently performed intervention in KM was acupuncture (injection). For pharmacological treatment, analgesics and anti-inflammatory medications were most frequently prescribed. The findings can be useful for health policymakers and as foundational data for clinicians and researchers.