Background: Despite advances in neurosurgical management, aneurismal subarachnoid hemorrhage (aSAH) still has high mortality and morbidity. This study aimed to clarify how delaying hospital admission after aSAH contributes to worse prognosis even today and to find the possibility for an improvement of its prognosis by early admission.
Methods: Four hundred twenty-one consecutive patients are the basis for this study. Cause of delay was classified into 5 categories: patient delay (PD), doctor delay (DD), transportation delay (TD), no delay (ND) (within 2 hours of onset), and others. Condition of each patient was assessed at time of onset and admission using H&K. The relationships between cause of delay and worsening of Hunt and Kosnik grading (H&K) were examined.
Results: The median delay time was 1.7 days. Only 41% of patients visited our institution without delay. Admission delay, especially PD and DD, exhibited a significant correlation to worsening of H&K. In addition to nondirect admission, misdiagnosis or delayed diagnosis contributed significantly to worsening of H&K. Incidence of DD has declined in recent years, whereas that of PD has increased. Consequently, no change in total number of delays was found.
Conclusions: There remains much room for an improvement of prognosis for aSAH by early admission. We need to fully realize this reality and to directly face this problem.
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http://dx.doi.org/10.1016/j.surneu.2005.10.025 | DOI Listing |
Med Sci Monit
December 2024
Department of Neurology, HangZhou Third People's Hospital, Hangzhou, Zhejiang, China.
BACKGROUND This study aimed to analyze the risk factors of central nervous system (CNS) infection caused by reactivation of varicella zoster virus (VZV) and provide reference for the prevention and early diagnosis of VZV-associated CNS infection. MATERIAL AND METHODS A prospective study was conducted on 1030 patients with acute herpes zoster (HZ) admitted to our hospital from January 2021 to June 2023. According to clinical manifestations and auxiliary examinations, they were divided into HZ group of 990 patients and VZV-associated CNS infection group of 40 patients.
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December 2024
Department of Information Security, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
In Internet of Things (IoT) networks, identifying the primary Medium Access Control (MAC) layer protocol which is suited for a service characteristic is necessary based on the requirements of the application. In this paper, we propose Energy Efficient and Group Priority MAC (EEGP-MAC) protocol using Hybrid Q-Learning Honey Badger Algorithm (QL-HBA) for IoT Networks. This algorithm employs reinforcement agents to select an environment based on predefined actions and tasks.
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December 2024
School of Civil Engineering and Architectures, Shandong University of Science and Technology, Qingdao, 266590, People's Republic of China.
The impact of rock bolts on the mechanical behavior of nonpersistent joints, including the intricate interactions between the joints, rock bridges, and rock bolts, has received limited investigation despite their effectiveness in reinforcing rock mass discontinuities. In order to tackle this issue, a variety of normal stresses were applied during direct shear tests conducted on artificial rock-like specimens with nonpersistent joints, both bolted and unbolted. Meanwhile, to measure the deformation in the rock bridge and joint plane region, a set of strain gauges were implemented.
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December 2024
Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
Cine-magnetic resonance imaging (MRI) has been used to track respiratory-induced motion of the liver and tumor and assist in the accurate delineation of tumor volume. Recent developments in compressed sensitivity encoding (SENSE; CS) have accelerated temporal resolution while maintaining contrast resolution. This study aimed to develop and assess hepatobiliary phase (HBP) cine-MRI scans using CS.
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December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
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