Clinical and Experimental Emergency Medicine

As aging progresses, clinical characteristics of elderly patients in the emergency department (ED) vary by age. We aimed to study differences among elderly patients in the ED by age group.For 2 years, patients aged 65 and older were enrolled in the study and classified into three groups: youngest-old, ages 65 to 74 years; middle-old, 75 to 84 years; and oldest-old, ≥85 years. Participants' sex, reason for ED visit, transfer from another hospital, results of treatment, type of admission, admission department and length of stay were recorded.During the study period, a total 64,287 patients visited the ED; 11,236 (17.5%) were aged 65 and older, of whom 14.4% were 85 and older. With increased age, the female ratio (51.5% vs. 54.9% vs. 69.1%, P<0.001), medical causes (79.5% vs. 81.3% vs. 81.7%, P=0.045), and admission rate (35.3% vs. 42.8% vs. 48.5%, P<0.001) increased. Admissions to internal medicine (57.5% vs. 59.3% vs. 64.7%, P<0.001) and orthopedic surgery (8.5% vs. 11.6% vs. 13.8%, P< 0.001) also increased. The ratio of admission to intensive care unit showed no statistical significance (P=0.545). Patients over age 85 years had longer stays in the ED (330.9 vs. 378.9 vs. 407.2 minutes, P<0.001), were discharged home less (84.4% vs. 78.9% vs. 71.5%, P<0.001), and died more frequently (6.3% vs. 10.4% vs. 13.0%, P<0.001).With increased age, the proportion of female patients and medical causes increased. Rates of admission and death increased with age and older patients had longer ED and hospital stays.

Sang Bum Lee

Jae Hun Oh

Jeong Ho Park

Seung Pill Choi

Jung Hee Wee

Differences in youngest-old, middle-old, and oldest-old patients who visit the emergency department

急诊科就诊的最年轻老年、中老年和最老年患者的差异

Over the past decades, the field of machine learning (ML) has made great strides in medicine. Despite the number of ML-inspired publications in the clinical arena, the results and implications are not readily accepted at the bedside. Although ML is very powerful in deciphering hidden patterns in complex critical care and emergency medicine data, various factors including data, feature generation, model design, performance assessment, and limited implementation could affect the utility of the research. In this short review, a series of current challenges of adopting ML models to clinical research will be discussed.

Cyra-Yoonsun Kang

Joo Heung Yoon

Current challenges in adopting machine learning to critical care and emergency medicine

采用机器学习应对重症监护和急诊医学的当前挑战

Hussein Omari Sombi

Clin Exp Emerg Med

Barriers to utilisation of Intraosseous vascular access in Paediatric Emergencies

儿科急诊中骨内血管通路应用的障碍

Robert Hlavin

Michael Gottlieb

Single Lumen Displacement of a Hemodialysis Catheter into the Azygous Vein on a Chest Radiograph

胸部X线片上单腔透析导管移位至奇静脉

Noriatsu Ohtsuka

Toshihiro Hatakeyama

Atsuki Hayamizu

Case of multiple acute cerebral infarctions after blunt cerebrovascular injuries

钝性脑血管损伤后多发性急性脑梗死病例

To explore and analyze pediatric trauma care practices across designated pediatric trauma centers (PTCs) in Switzerland. The focus is on reception, trauma team activation (TTA), trauma team composition, patient volumes, and infrastructure.

Leopold Simma

Pediatric Trauma Management in Switzerland: Insights from a Nationwide Survey

瑞士儿童创伤管理：全国调查的见解

Annapaola Truono

Nicola Romano

Lorenzo Bacigalupo

Antonio Castaldi

Acute iodinate contrast medium reaction: look at the CT images!

急性碘造影剂反应：看看CT图像！

This study aimed to identify and analyze the factors influencing Emergency Department Length of Stay (ED LOS) using a nationwide database to improve emergency care efficiency.

Minha Kim

Sujeong Lee

Minyoung Choi

Doyeop Kim

Junsang Yoo

Tae Gun Shin

Jin-Hee Lee

Seongjung Kim

Hansol Chang

Eunsil Ko

Predictive Factors of Emergency Department Length of Stay: Analyzing National Emergency Department Data

急诊科停留时间的预测因素：分析全国急诊科数据

Abbreviation	Clin Exp Emerg Med
Journal Impact	1.88
Quartiles(Global)	EMERGENCY MEDICINE(Q2)

ISSN	2383-4625, 3022-1811
h-index	16

Publication Information	Publisher: Korean Society of Emergency Medicine，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2018，Proportion of original research papers: 94.00%，Self Citation Rate:15.80%， Gold OA Rate: 97.39%
Average review cycle	20 weeks

Clinical and Experimental Emergency Medicine

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Differences in youngest-old, middle-old, and oldest-old patients who visit the emergency department

Current challenges in adopting machine learning to critical care and emergency medicine

Barriers to utilisation of Intraosseous vascular access in Paediatric Emergencies

Single Lumen Displacement of a Hemodialysis Catheter into the Azygous Vein on a Chest Radiograph

Case of multiple acute cerebral infarctions after blunt cerebrovascular injuries

Single Lumen Displacement of a Hemodialysis Catheter into the Azygous Vein on a Chest Radiograph

Case of multiple acute cerebral infarctions after blunt cerebrovascular injuries

Pediatric Trauma Management in Switzerland: Insights from a Nationwide Survey

Acute iodinate contrast medium reaction: look at the CT images!

Predictive Factors of Emergency Department Length of Stay: Analyzing National Emergency Department Data