Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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This study sought to explore and externally validate the Carpenter instrument's efficacy in predicting postdischarge fall risk among older adults admitted to the emergency department (ED) for reasons other than falls or related injuries. ⋯ While the Carpenter instrument associated with a higher 6-month postadmission fall risk among older adults post-ED visit, its accuracy for individual patient decision making was limited. Given the significant impact of falls on health outcomes and health care costs, refining risk assessment tools remains essential. Future research should focus on enhancing these assessments and devising targeted proactive strategies.
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Federal regulations allow exception from informed consent (EFIC) to study emergent conditions when obtaining prospective consent is not feasible. Little is known about public views on including children in EFIC studies. The Pediatric Dose Optimization for Seizures in EMS (PediDOSE) trial implements age-based, standardized midazolam dosing for pediatric seizures. The primary objective of this study was to determine public support for and concerns about the PediDOSE EFIC trial. The secondary objective was to assess how support for PediDOSE varied by demographics. ⋯ In communities where this study will occur, most respondents supported PediDOSE being conducted with EFIC and most approved of children being enrolled without prior consent. Support was lowest among non-Hispanic Black respondents and highest among health care providers. Further research is needed to determine optimal ways to address the concerns of specific racial and ethnic groups when conducting EFIC trials.
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Review Meta Analysis
Using natural language processing in emergency medicine health service research: A systematic review and meta-analysis.
Natural language processing (NLP) represents one of the adjunct technologies within artificial intelligence and machine learning, creating structure out of unstructured data. This study aims to assess the performance of employing NLP to identify and categorize unstructured data within the emergency medicine (EM) setting. ⋯ Our analysis revealed a generally favorable performance accuracy in using NLP within EM research, particularly in the realm of radiologic interpretation. Consequently, we advocate for the adoption of NLP-based research to augment EM health care management.