Background: Balance and gait impairments are the most common motor deficits due to stroke, limiting the patients' daily life activities and participation in society. Studies investigating effect of task-specific training using biomechanical balance and gait variables (i.e. kinetic and kinematic parameters) as well as posturography after stroke are scarce.
Objectives: The primary aim of this study is to assess the efficacy and long-term outcome of task-specific training based on motor relearning program (MRP) on balance, mobility and performance of activities of daily living among post-stroke patients.
Methods: In this two-armed randomised controlled clinical trial, a total of 66 sub-acute stroke patients who meet the trial criteria will be recruited. The patients will randomly receive task-specific training based on MRP or a conventional physical therapy program (CPT). Twenty-four physiotherapy sessions will be conducted, divided into three training sessions per week, 1 h per session, for 8 weeks, followed by an analysis of changes in patient's balance, gait and performance of activates of daily living at three time periods; baseline, post-intervention and follow-up after 3-months, using clinical outcome measures and instrumental analysis of balance and gait.
Discussion: The results of this study can guide to better understanding and provide an objective clinical basis for the use of task-specific training in stroke rehabilitation. Also, it intends to help bridge the current knowledge gap in rehabilitation and training recommendations to provide a therapeutic plan in post-stroke rehabilitation.
Trial Registration: ClinicalTrials.gov (NCT05076383). Registered on 13 October 2021 (Protocol version: v2.0).
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http://dx.doi.org/10.1177/23969873211061027 | DOI Listing |
J Imaging
December 2024
PolitoBIOMed Lab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy.
Skin cancer is among the most prevalent cancers globally, emphasizing the need for early detection and accurate diagnosis to improve outcomes. Traditional diagnostic methods, based on visual examination, are subjective, time-intensive, and require specialized expertise. Current artificial intelligence (AI) approaches for skin cancer detection face challenges such as computational inefficiency, lack of interpretability, and reliance on standalone CNN architectures.
View Article and Find Full Text PDFJ Orthop Sci
December 2024
The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, China; Department of Orthopaedic Surgery, Fuzhou Second Hospital, Fuzhou, Fujian, China.
Background: Hip fracture affects millions of persons and is associated with excess morbidity and mortality. More knowledge is needed to regard the prolonged effects of intensive exercise in relatively frail hip fracture patients. In this meta-analysis, we want to determine whether intensity strength training in patients after hip fracture is superior to general exercises in improving physical function.
View Article and Find Full Text PDFBMC Anesthesiol
December 2024
Department of Anesthesiology, Ningbo No.2 Hospital, No.41, Northwest Street, Ningbo, 315010, P.R. China.
Background: Developing proficiency in ultrasound-guided nerve block (UGNB) demands an intricate understanding of cross-sectional anatomy as well as spatial reasoning, which is a big challenge for beginners. The aim of this pilot study was to evaluate the feasibility of virtual reality (VR)-facilitated anatomy education in the first performance of ultrasound-guided interscalene brachial plexus blockade among novice anesthesiologists. We carried out pilot testing of this hypothesis using a prospective, single blind, randomized controlled trial.
View Article and Find Full Text PDFbioRxiv
December 2024
Machine Learning Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Pre-trained language models have transformed the field of natural language processing (NLP), and their success has inspired efforts in genomics to develop domain-specific foundation models (FMs). However, creating high-quality genomic FMs from scratch is resource-intensive, requiring significant computational power and high-quality pre-training data. The success of large language models (LLMs) in NLP has largely been driven by industrial-scale efforts leveraging vast, diverse corpora and massive computing infrastructure.
View Article and Find Full Text PDFJ Am Med Inform Assoc
December 2024
CEIEC, Universidad Francisco de Vitoria, Pozuelo de Alarcón, 28223 Madrid, Spain.
Objectives: We evaluate the effectiveness of large language models (LLMs), specifically GPT-based (GPT-3.5 and GPT-4) and Llama-2 models (13B and 7B architectures), in autonomously assessing clinical records (CRs) to enhance medical education and diagnostic skills.
Materials And Methods: Various techniques, including prompt engineering, fine-tuning (FT), and low-rank adaptation (LoRA), were implemented and compared on Llama-2 7B.
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