Emergency medicine journal : EMJ
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Randomized Controlled Trial
Commencing one-handed chest compressions while activating emergency medical system using a handheld mobile device in lone-rescuer basic life support: a randomised cross-over simulation study.
In conventional basic life support (c-BLS), a lone rescuer is recommended to start chest compressions (CCs) after activating the emergency medical system. To initiate earlier CCs in lone-rescuer BLS, we designed a modified BLS (m-BLS) sequence in which the lone rescuer commences one-handed CCs while calling for help using a handheld cellular phone with the other free hand. This study aimed to compare the quality of BLS between c-BLS and m-BLS. ⋯ In simulated lone-rescuer BLS, the m-BLS could deliver significantly earlier CCs than the c-BLS while maintaining high-quality cardiopulmonary resuscitation.
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Randomized Controlled Trial Observational Study
Biomechanical analysis of force distribution in one-handed and two-handed adult chest compression: a randomised crossover observational study.
The standard method of chest compression for adults is a two-handed procedure. One-handed external chest compression (ECC) is used in some situations such as during transport of patients who had an out-of-hospital cardiac arrest, but the quality of one-handed ECC is still not well known. The distribution of force is related to the quality of chest compression and may affect the risk of injury. This study aimed to determine the differences in the quality and potential safety concern between one-handed ECC and two- handed ECC. ⋯ The quality of one-handed ECC, based on depth and recoil, is worse than that of standard two-handed ECC. The pressure and force distribution of one-handed ECC result in greater ulnar pronation of the hand than that of two-handed ECC. One-handed ECC more easily causes operator fatigue. Acknowledging these findings and adjusting training for one-handed ECC would potentially improve the quality of cardiopulmonary resuscitation during transport.
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There has been a rise in the number of studies relating to the role of artificial intelligence (AI) in healthcare. Its potential in Emergency Medicine (EM) has been explored in recent years with operational, predictive, diagnostic and prognostic emergency department (ED) implementations being developed. ⋯ Our linked manuscript provided a conceptual framework (including a glossary of AI terms) to support clinicians in interpreting AI research. The aim of this paper is to supplement that framework by exploring the key issues for clinicians and researchers to consider in the process of developing an AI model.