Abstracts

Hematoma Blocks Effective for Closed Forearm Reduction Take Home Point: Hematoma blocks are an effective method of achieving analgesia to facilitate closed reduction of forearm fractures. Citation: Pitman G, Soeyland T, Popovic G, et al. Hematoma block is the most efficient technique for closed forearm fracture reduction: a retrospective cohort study. Emerg Med J. 2024; 41:595–601 Relevance: Adequate closed reduction of wrist and forearm fractures acutely after injury is important to reduce risk of complications and need for surgery. Closed wrist and forearm fractures are common urgent care (UC) presentations …
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ECG Interpretation: Humans Vs Machines Take Home Point: In this study, cardiologists and emergency physicians (EP) had greater accuracy for electrocardiogram (ECG) interpretation than current versions of ChatGPT and Gemini. Citation: Günay S, Öztürk A, Yiğit Y. The accuracy of Gemini, GPT-4, and GPT-4o in ECG analysis: A comparison with cardiologists and emergency medicine specialists. Am J Emerg Med. 2024 Oct:84:68-73. doi: 10.1016/j.ajem.2024.07.043. Relevance: Artificial Intelligence (AI) is rapidly evolving and becoming an integral part of clinical medicine and research with large-language models (LLM) being evaluated for many novel applications …
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Sophistication of ECG for Detection of Acute Coronary Syndromes Take Home Point: Cardiac electrical biomarker (CEB), a finding detectable on ECG, may hold potential for identifying patients with acute myocardial ischemia; this may have significant implications for urgent care (UC) based chest pain risk stratification. Citation: Chattopadhyay S, Adjei F, Kardos A. Changes in Cardiac Electrical Biomarker in Response to Coronary Arterial Occlusion: An Experimental Observation. J Cardiovasc Transl Res. 2024 Aug;17(4):870-878. doi: 10.1007/s12265-024-10487-w. Relevance: Evaluation of chest pain is particularly challenging in UC where immediately available troponin blood testing …
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Public Perceptions of Artificial Intelligence Use in Healthcare Take Home Point: Patient and healthcare workers surveyed were generally accepting of the use of artificial intelligence (AI) in medicine. Respondents did express some concern about the potential impact of AI on the accuracy of medical decision-making, however. Citation: Thornton N, Binesmael A, Horton T, et al. AI in health care: what do the public and NHS staff think? The Health Foundation. Published July 31, 2024. Accessed September 5, 2024. https://www.health.org.uk/publications/long-reads/ai-in-health-care-what-do-the-public-and-nhs-staff-think Relevance: AI is encountering rapid adoption within healthcare for various applications …
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Can Large Language Models Help in Assessing Acuity of Patients Presenting to ED? Take Home Point: Integration of large language models (LLMs) in the emergency department (ED) could enhance triage processes. This warrants further investigation particularly in the urgent care (UC) space. Citation: Williams C, Zack T, Miao B, et. al. Use of a Large Language Model to Assess Clinical Acuity of Adults in the Emergency Department. JAMA Netw Open. 2024 May 1;7(5): e248895. doi: 10.1001/jamanetworkopen.2024.8895. PMID: 38713466; PMCID: PMC11077390. Relevance: As we start to investigate the applications of artificial …
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