Background: As interfacility transfer of patients with stroke becomes increasingly common, understanding fluctuations in deficits during transfer may help predict resource needs. We sought to characterize changes in NIH Stroke Scale (NIHSS) scores during transfer and identify factors associated with early rapid improvement (ERI).
Methods: We used prospectively collected data from our Comprehensive Stroke Center's (CSCs) stroke and telestroke network databases. We calculated changes in NIHSS scores for all patients transferred to our CSC after an initial telestroke evaluation from January 2010 to December 2015. Logistic regression identified factors associated with ERI, controlling for patient characteristics available on arrival.
Results: Among the 505 patients included, the median initial NIHSS score was 11 (interquartile range [IQR] 5-18), and on CSC arrival, it was 9 (IQR 3-17), with a median change of 0 (-3 to -0). Of note, 74.5% of scores changed by fewer than 4 points (7% increased ≥4 points, and 19% decreased ≥4). In 85% of cases, the NIHSS score change did not cross a threshold to alter eligibility for thrombectomy. In multivariable modeling, ERI was associated with ability to ambulate before the index stroke (odds ratio [OR] 5.79, = 0.02) and higher initial NIHSS (OR 1.06 per point, = 0.001).
Conclusions: These findings may be valuable for resource planning and for inclusion in thrombectomy alert activation processes at the receiving hospital.
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http://dx.doi.org/10.1212/CPJ.0000000000000667 | DOI Listing |
Cell Div
January 2025
Babak Myeloma Group, Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic.
Background: Multiple myeloma (MM) represents the second most common hematological malignancy characterized by the infiltration of the bone marrow by plasma cells that produce monoclonal immunoglobulin. While the quality and length of life of MM patients have significantly increased, MM remains a hard-to-treat disease; almost all patients relapse. As MM is highly heterogenous, patients relapse at different times.
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January 2025
Division of Infectious Diseases and Tropical Medicine, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, 110 Intavaroros Rd., Muaeng, Chiang Mai, 50200, Thailand.
Early diagnosis and appropriate treatment are essential for reducing morbidity and mortality in tuberculous meningitis (TBM). This study aimed to evaluate the diagnostic performance of the Xpert MTB/RIF assay for the diagnosis of TBM in patients with subacute lymphocytic meningitis. This cross-sectional study included 65 cerebrospinal fluid (CSF) specimens from patients at Maharaj Nakorn Chiang Mai University Hospital, Thailand, between January 2015 and March 2016.
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January 2025
Department of Ophthalmology, Faculty of Medicine for Girls, Al-Azhar University, Cairo, Egypt.
Peribulbar anesthesia is mainly used for cataract surgery. Many studies had used atracurium and rocuronium as an additive to the local anesthetic (LA) drugs in eye surgery. The aim of this study is to evaluate the efficacy of adding atracurium versus rocuronium to a local anesthetic mixture, in providing an early onset of orbital akinesia and corneal anesthesia during cataract surgery.
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January 2025
Department of Orthopaedic Surgery, Virginia Commonwealth University Health System, Richmond, VA, USA.
Introduction: Outpatient total knee arthroplasty (TKA) has quickly grown in popularity, largely driven by policy shifts and the recent coronavirus disease 2019 (COVID-19) pandemic. The aim of this study was to compare 30-day complications between outpatient TKA (oTKA) versus inpatient TKA (iTKA) before and after the COVID-19 pandemic to elucidate the effect of the pandemic on utilization and short-term outcomes.
Methods: Patients who underwent primary TKA between 2008 and 2021 were identified through Current Procedural Terminology codes in a national database.
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
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