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Early Prediction of Standing at Discharge in Moderate-to-Severe TBI: A Clinical Machine Learning Model Integrating Modifiable and Nonmodifiable Factors
1Division of Physical Therapy, Department of Physical Medicine and rehabilitation, National Taiwan University Hospital, Taipei, Taiwan 2Doctoral Program in Big Data Analytics for Industrial Applications, National Chung Hsing University, Taichung, Taiwan 3Department of Applied Mathematics and Institute of Statistics, National Chung Hsing University, Taichung, Taiwan 4Department of Traumatology, National Taiwan University Hospital, Taipei, Taiwan 5Department of Cardiovascular Surgery, National...
SERS-Based Universal AST: Rapid Treatment Guidance for Blood-Culture Bacteria before Species Identification
Introduction Click to copy section linkSection link copied! Bacterial bloodstream infection leading to sepsis remains a significant clinical challenge, (1) with mortality rates of 23.75% for in-hospital cases, 41.55% at six months, (2) and 8 to 48% at one year. (3) Rapid initiation of appropriate antimicrobial therapy is therefore critical, as each hour of delay is associated with an approximately 5% increases in mortality risk.
Rapid antibiotic susceptibility testing of bacteria from patients’ blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy
Abstract Blood stream infection is one of the major public health issues characterized with high cost and high mortality. Timely effective antibiotics usage to control infection is crucial for patients’ survival. The standard microbiological diagnosis of infection however can last days. The delay in accurate antibiotic therapy would lead to not only poor clinical outcomes, but also to a rise in antibiotic resistance due to widespread use of empirical broad-spectrum antibiotics.
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