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Pitfalls of the E-Ref Procedure: Tie Values and the Proportion of the Abnormal Data.
Muscle Nerve
January 2025
Department of Neurology, Teikyo University School of Medicine, Tokyo, Japan.
Introduction: Extrapolated reference values (E-Ref) procedure is a new method for determining the cutoff value without collecting the control data. We tried to apply this method to determine the cutoff value for the distal motor latency of the median nerve (median DML). During this process, we found two pitfalls of the E-Ref method.
View Article and Find Full Text PDFIntraoperative rescue of a dislodged renal stent during fenestrated endovascular aortic repair for treatment of type 1A endoleak.
J Vasc Surg Cases Innov Tech
February 2025
Division of Vascular and Endovascular Surgery, Cardio-Thoracic-Vascular Department, Integrated University Healthcare Giuliano-Isontina, University Hospital of Cattinara, Trieste, Italy.
In the past 15 years, fenestrated-branched endovascular aortic repair (F-BEVAR) has progressively become the first-line option for management of most complex abdominal aortic aneurysms (AAAs); with increasing experience, as well as persistent technological refinements, F-BEVAR indications have been expanded to include rescue of failures after prior EVAR. Despite the feasibility and effectiveness, F-BEVAR procedures in the presence of prior infrarenal endografts may come with higher technical complexity that should be properly anticipated, and several anatomical challenges can be expected. Among these, presence of suprarenal bare stents from prior EVAR device are certainly a frequent scenario and may sometimes make target vessel cannulation more difficult because of encroachment on the target vessel origins.
View Article and Find Full Text PDFParticipation and Compliance in a Multimodal Prehabilitation Program for Colorectal Cancer (PACE): A Qualitative Study.
Patient Prefer Adherence
December 2024
Department of Surgery, Zuyderland Medical Center, Heerlen, 6419 PC, The Netherlands.
Purpose: Interest in prehabilitation, the preoperative enhancement of patients' condition, is rising in the surgical field. Challenging factors appear to be patients' motivation to participate in and their compliance with prehabilitation programs. The aim of this qualitative study was to study the real-life experience of prehabilitation by assessing the lived experience and perceptions of participants in a multimodal prehabilitation program and to explore factors that influence participation and compliance during prehabilitation.
View Article and Find Full Text PDFLatent profiles of problem-solving skills and their association with depressive symptoms in parents of children with cancer: A cross-sectional study.
Asia Pac J Oncol Nurs
December 2025
Xiangya School of Nursing, Central South University, Changsha, Hunan, China.
Objective: Depressive symptoms are prevalent among parents of children with cancer, significantly impacting their well-being. Problem-solving skills, strongly linked to depressive symptoms, offer a promising avenue for intervention. This study aimed to identify latent profiles of parental problem-solving skills and evaluate differences in depressive symptoms across these profiles.
View Article and Find Full Text PDFDesign of an improved graph-based model for real-time anomaly detection in healthcare using hybrid CNN-LSTM and federated learning.
Heliyon
December 2024
Department of Computer Science & Engineering, K L E F Deemed To Be University, Green Fields, Vaddeswaram, Guntur (dt), Andhra Pradesh, 521230, India.
Real-time monitoring and anomaly detection are essential in healthcare to ensure safe conditions for patients and maintain the integrity of medical data samples. The majority of existing systems, despite improvements in healthcare technologies, cannot capture the spatial and temporal patterns of multimodal data simultaneously, process high Volume data in real-time, and ensure the privacy of patients' identity effectively. In this work, we handle these limitations by proposing a complete approach that uses state-of-the-art deep learning and data processing architectures to realize resilient anomaly detection in healthcare systems.
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