Clinical and Experimental Emergency Medicine最新文献

{"title":"Evolution of Targeted Temperature Management Duration: From a One-Size-Fits-All Approach to Precision Medicine.","authors":"Dong Hun Lee, Byung Kook Lee","doi":"10.15441/ceem.26.440","DOIUrl":"https://doi.org/10.15441/ceem.26.440","url":null,"abstract":"","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contemporary Approaches to Prehospital Acute Coronary Syndrome Risk Stratification: A Narrative Review.","authors":"Anna C Meyer, Jacob J Frank, Melanie Mingle, Sharon Klapec, Vince LaMantia","doi":"10.15441/ceem.26.182","DOIUrl":"https://doi.org/10.15441/ceem.26.182","url":null,"abstract":"<p><p>Chest pain is a common high-risk complaint encountered by emergency medical services (EMS). Early identification and treatment of acute coronary syndromes (ACS) improve outcomes, yet prehospital evaluation remains constrained by limited diagnostic resources. This narrative review examines contemporary approaches to prehospital ACS risk stratification, diagnostic technologies, and decision-making. A targeted PubMed search of peer-reviewed publications and selected grey literature published between 2020 and 2025 was supplemented by targeted searches and citation review. Relevant literature published after the initial search period was incorporated during manuscript preparation and revision. Studies addressing prehospital ACS risk stratification, electrocardiogram (ECG) assessment, point-ofcare biomarkers, artificial intelligence (AI)-assisted diagnostics, and systems-level cardiac triage were included and synthesized thematically. Biomarker-integrated pathways, particularly HEART-derived approaches combined with point-of-care troponin testing, demonstrated the strongest evidence for safe prehospital risk assessment and reduced healthcare utilization. Biomarker-free approaches offered broader applicability but lower diagnostic certainty. ECG acquisition and transmission remained foundational for STEMI recognition and activation, while AI-assisted ECG interpretation and decision-support tools showed promising diagnostic performance but lacked prospective outcome-based validation. Gaps remain regarding atypical presentations, sex-specific strategies, and validation across diverse EMS environments. Overall, contemporary evidence supports structured prehospital risk stratification as decision support rather than definitive diagnosis. Implementation depends on provider training, protocol standardization, resources, and integration within regional systems of care. Future research should prioritize validation across diverse EMS settings, atypical and sex-specific presentations, and prospective evaluation of emerging diagnostic technologies.</p>","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in sepsis care and research: aligning clinical questions with model selection and human oversight.","authors":"Sukyo Lee, Sumin Jung, Sejoong Ahn","doi":"10.15441/ceem.26.368","DOIUrl":"https://doi.org/10.15441/ceem.26.368","url":null,"abstract":"","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Language Models in the Emergency Department: Fairness Implications for Clinicians.","authors":"Kasumi Satoh, Manabu Okuyama, Hajime Nakae, Yohei Okada","doi":"10.15441/ceem.26.409","DOIUrl":"https://doi.org/10.15441/ceem.26.409","url":null,"abstract":"<p><p>Applications of artificial intelligence (AI), particularly large language models (LLMs), are rapidly expanding in emergency medicine, but concerns remain regarding their fairness across diverse patient populations. Fairness extends beyond model accuracy and includes equal performance across patient groups, equitable outcomes and allocation of clinical benefits, and procedural fairness throughout the lifecycle of AI implementation. This narrative review aims to provide a practical framework for understanding and evaluating fairness in emergency medicine applications of AI and LLMs. This narrative review organizes fairness risks into three clinically relevant layers. First, datalevel fairness concerns the data used to train, fine-tune, or prompt AI systems. Biased clinical data, including non-random missingness and stigmatizing documentation, can influence model performance. Second, output-level fairness concerns whether the outputs generated by AI systems are equally accurate, safe, and useful across patient groups. In emergency care, subgroup-specific errors in recommendations, predictions, translations, or instructions may arise from aggregation, learning, or evaluation bias, even when overall model performance appears strong. Third, human-interaction fairness concerns whether the benefits of AI are realized equitably in clinical practice and by patients. Even when data and outputs are fair, inequities may arise if clinicians over-rely on or reject useful AI recommendations, or if patients are unable to access, understand, or trust AI-mediated information. Clinicians should consider potential implications for safety, transparency, and fairness alongside overall model accuracy when evaluating AI tools in emergency medicine.</p>","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Propensity score methods in medical research: a concise practical guide with applications to emergency medicine.","authors":"Han Zo Choi, Jongkyeong Kang, Sook-Young Woo","doi":"10.15441/ceem.26.437","DOIUrl":"https://doi.org/10.15441/ceem.26.437","url":null,"abstract":"<p><p>Propensity score (PS) methods can reduce measured confounding in observational emergency medicine research, but validity depends more on design choices and diagnostics than on the treatment-prediction model. This clinical review presents a practical workflow for defining the target estimand, selecting pretreatment covariates, choosing matching or weighting, checking covariate balance and treatment overlap, and analyzing a binary outcome in the population created by the PS design. The average treatment effect (ATE), average treatment effect in the treated (ATT), and average treatment effect in the overlap population (ATO) answer different clinical questions. A worked example of 1,200 simulated emergency patients uses a common, explicit time zero to show how confounding by indication can make immediate treatment appear harmful before adjustment and how PS matching, stabilized inverse probability of treatment weighting, and overlap weighting change both the comparison population and estimated effect. Standardized mean differences, PS distributions, weight summaries, and effective sample size are presented before treatment-specific risks, risk differences, and risk ratios. A standalone supplement provides package-based R code, expected output, and analysis-derived figures. Survival analysis is outside the scope. PS methods are most credible when used as design tools: define the question, construct a supported comparison, verify diagnostics, and only then analyze outcomes.</p>","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large language models as clinical decision support in emergency medicine: promise, pitfalls, and the risk of uncritical adoption.","authors":"Chiwon Ahn","doi":"10.15441/ceem.26.352","DOIUrl":"https://doi.org/10.15441/ceem.26.352","url":null,"abstract":"","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond Parametric and Nonparametric Tests: A Practical Guide to Table 1 in Clinical Research.","authors":"Jongkyeong Kang, Sook-Young Woo, Han Zo Choi","doi":"10.15441/ceem.26.333","DOIUrl":"https://doi.org/10.15441/ceem.26.333","url":null,"abstract":"<p><p>In many clinical research reports, Table 1 presents the baseline or general characteristics of study participants and helps readers understand the study population and observed between-group differences. Despite its apparent simplicity, constructing and interpreting Table 1 requires several distinct decisions, including whether formal between-group inference is scientifically justified, how variables should be summarized, and how observed differences should be interpreted. Routine significance testing of baseline characteristics is generally discouraged in randomized trials and often provides limited information in observational studies, where group differences may reflect treatment selection, exposure patterns, disease severity, or other systematic mechanisms. We propose a purpose-first framework in which investigators first clarify the intended role of Table 1, consider the study design, and determine whether a comparison addresses a prespecified and scientifically meaningful inferential question. They should then identify the variable type, evaluate distributional shape, influential observations, and clinical interpretability, and select appropriate summary statistics. For descriptive applications, transparent group-specific summaries should be emphasized, with observed absolute or standardized differences added when useful and without routine p-values. Confidence intervals should be reported when a population-level estimation objective has been defined. When formal inference is justified, investigators should define the target quantity or estimand and select a statistical method according to the variable type, data structure, distributional characteristics, and model assumptions. Effect estimates and confidence intervals should receive greater emphasis than isolated p-values. This framework distinguishes description from inference and promotes transparent, statistically coherent, and clinically meaningful construction and interpretation of Table 1.</p>","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":""},"PeriodicalIF":3.4,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149191755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pregnant women's experiences and perceptions of seat belt education: assessing the impact of maternity seat belt safety interventions.","authors":"Soo Yeon Sohn, Sejin Heo, Hee Yoon, Won Chul Cha, Se Uk Lee, Gun Tak Lee, Jong Eun Park, Hansol Chang, Minha Kim, Seung Jin Maeng, Taerim Kim","doi":"10.15441/ceem.25.194","DOIUrl":"10.15441/ceem.25.194","url":null,"abstract":"<p><strong>Objective: </strong>Trauma is one of the leading causes of obstetric morbidity and mortality, and incorrect seat belt use is a major determinant of adverse outcomes. This study evaluated the impact of structured education on proper seat belt use during pregnancy and examined preferences for future interventions.</p><p><strong>Methods: </strong>This prospective pilot study was conducted at a single center in Korea, over an 8-week period. Pregnant women at <37 weeks of gestation who visited the outpatient obstetrics and gynecology department were enrolled. A pre-education survey assessed baseline seat belt practices. Participants then received structured education, and a post-education survey 1 month later evaluated changes in awareness and behavior.</p><p><strong>Results: </strong>Sixty participants were included in the final analysis. Most reported obtaining seat belt information from unverified sources, particularly online communities. Many indicated a preference for structured education delivered by obstetricians, especially early in pregnancy. After the intervention, knowledge of proper seat belt use increased from 21.7% to 85.0% (P<0.001), and correct seat belt usage rose from 16.7% to 88.3% (P<0.001). Seat belt use while driving also improved markedly, increasing from 19.5% to 78.0% (P<0.001), demonstrating the program's effectiveness in enhancing awareness and behavior.</p><p><strong>Conclusion: </strong>Education on seat belt use during pregnancy significantly improved both knowledge and correct usage among pregnant women. Routine, formal educational programs are recommended to promote maternal and fetal safety.</p>","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":"318-325"},"PeriodicalIF":3.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147303239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-artificial intelligence collaboration in emergency medical services: from diagnostic algorithms to shared decision support.","authors":"Hang A Park","doi":"10.15441/ceem.26.398","DOIUrl":"10.15441/ceem.26.398","url":null,"abstract":"","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":"13 3","pages":"350-354"},"PeriodicalIF":3.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654664/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149172151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence as cognitive support in emergency medicine.","authors":"Samita M Heslin, Adam J Singer","doi":"10.15441/ceem.26.104","DOIUrl":"10.15441/ceem.26.104","url":null,"abstract":"","PeriodicalId":10325,"journal":{"name":"Clinical and Experimental Emergency Medicine","volume":" ","pages":"254-257"},"PeriodicalIF":3.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13654679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147608209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}