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The coronavirus disease 2019 (COVID-19) pandemic is a major challenge for global healthcare systems. Early and safe triage in the emergency department (ED) is crucial for proper therapy. However, differential diagnosis remains challenging. Rapid antigen testing (RAT) may help to improve early triage and patient safety. We performed a retrospective study of 234 consecutive patients with suspected COVID-19 who presented to our ED in November 2020. All underwent SARS-CoV-2-nasopharyngeal swab testing using both RAT and reverse transcription polymerase chain reaction (RT-PCR). The inpatient treatment was established according to an empirically developed triage algorithm. The accuracy of the suggested algorithm was analyzed based on the rate of outpatients returning within 7 days and inpatients staying for less than 48 hours. COVID-19 inpatients and outpatients were compared for symptoms, vital signs, and C-reactive protein levels. Of the 221 included patients with suspected COVID-19 infection, the diagnosis could be confirmed in 120 patients (54.3%) by a positive RT-PCR result, whereas only 72% of those had a positive antigen test. Of the 56 COVID-19 outpatients, three returned within 7 days with the need for hospital treatment due to clinical deterioration. Among the 64 COVID-19 inpatients, 4 were discharged within 48 hours, whereas 60 stayed longer (mean duration 10.2 days). The suggested triage algorithm was safe and efficient in the first 234 consecutive patients. RAT can confirm a diagnosis in 72% of PCR proven COVID-19 patients and allows early cohort isolation as an important way to save hospital capacity.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9592145 | PMC |
http://dx.doi.org/10.1097/MD.0000000000031278 | DOI Listing |
Introduction: Effective triage in the emergency department (ED) is essential for optimizing resource allocation, improving efficiency, and enhancing patient outcomes. Conventional systems rely heavily on clinical judgment and standardized guidelines, which may be insufficient under growing patient volumes and increasingly complex presentations.
Methods: We developed a machine learning triage model, MIGWO-XGBOOST, which incorporates a Multi-strategy Improved Gray Wolf Optimization (MIGWO) algorithm for parameter tuning.
Pest Manag Sci
September 2025
AgResearch Ltd, Tuhiraki, Lincoln, New Zealand.
Background: Conventional weed risk assessments (WRAs) are time-consuming and often constrained by species-specific data gaps. We present a validated, algorithmic alternative, the model, that integrates climatic suitability ( ), weed-related publication frequency (P) and global occurrence data ( ), using publicly available databases and artificial intelligence (AI)-assisted text screening with a large language model (LLM).
Results: The model was tested against independent weed hazard classifications for New Zealand and California.
PLoS One
September 2025
Department of Obstetrics and Gynecology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
Cervical cancer remains the leading cause of cancer death among women in sub-Saharan Africa and is more severe in high HIV-burdened countries due to persistent high-risk human papillomavirus (hrHPV). In 2021, the World Health Organization recommended primary hrHPV testing for cervical cancer screening; however, optimal triage strategies following positive hrHPV tests remain unclear. We conducted a prospective cost analysis of triage methods for positive hrHPV results among women living with and without HIV in Gaborone, Botswana.
View Article and Find Full Text PDFPrehosp Disaster Med
September 2025
CACI, Inc, Falls Church, VirginiaUSA.
Introduction: Targeted identification, effective triage, and rapid hemorrhage control are essential for optimal outcomes of mass-casualty incidents (MCIs). An important aspect of Emergency Medical Service (EMS) care is field triage, but this skill is difficult to teach, assess, and research.
Study Objective: This study assessed triage efficacy and hemorrhage control of emergency responders from different professions who used the Sort, Assess, Life-Saving Treatment (SALT) triage algorithm in a virtual reality (VR) simulation of a terrorist subway bombing.