Letter to the EditorGait disorder is a particularly disabling and treatment refractory symptom of Parkinson's disease (PD), contributing to higher fall risk [1] and restrictions of daily activities [2]. Although Parkinsonian gait can improve with deep brain stimulation (DBS) of the subthalamic nucleus (STN) or globus pallidus internus (GPi) [3], gait is not considered to be adequately treated [4].Two "closed-loop" DBS alternatives have been proposed: adaptive DBS (aDBS), where stimulation varies with local field potential (LFP) power (e.g. beta-band 13e30 Hz) [5], and responsive DBS (rDBS), where stimulation is entrained to a specific phase of the pathological movement (e.g. tremor) [6]. Two studies have evaluated aDBS effect on gait [7,8], and none has evaluated rDBS effect on gait.Beta-band power is modulated with the gait cycle [9]. We hypothesize that rDBS timed to gait events might allow, or even enhance, this normal, physiological beta modulation, improving gait. We developed and tested a rDBS system delivering short duration pulse trains at specific gait phases in real-time. To assess the accuracy of stimulation delivery, gait phases were aligned with stimulation artifacts collected from a surface EMG electrode on the neck. To measure efficacy of this rDBS system, we assessed spatial and temporal gait metrics.Sixteen PD individuals with bilateral DBS leads (13 STN, 3 GPi) and Medtronic SC, PC, or RC implantable neural stimulators (INSs) were enrolled. All gave informed consent according to a University of Minnesota Institutional Review Board approved protocol. Data from four participants (all STN) were excluded due to technical difficulties (stimulation not delivered at the target gait phase). Detailed participant demographics are in Table S1.Participants walked on an instrumented treadmill (C-Mill, Motek Medical, Netherlands) for one-minute trials. Each trial was under one of five conditions: off-stimulation, continuous stimulation, stimulation triggered on ipsilateral heel-strike (IHS), on contralateral heel-strike (CHS), or on contralateral toe-off (CTO).
Reporting of sports-related concussions (SRCs) has risen dramatically over the last decade, increasing awareness of the need for treatment and prevention of SRCs. To date most prevention studies have focused on equipment and rule changes to sports in order to reduce the risk of injury. However, increased neck strength has been shown to be a predictor of concussion rate. In the TRAIN study, student-athletes will follow a simple neck strengthening program over the course of three years in order to better understand the relationship between neck strength and SRCs. Neck strength of all subjects will be measured at baseline and biannually over the course of the study using a novel protocol. Concussion severity and duration in any subject who incurs an SRC will be evaluated using the Sports Concussion Assessment Tool 5th edition, a questionnaire based tool utilizing several tests that are commonly affected by concussion, and an automated eye tracking algorithm. Neck strength, and improvement of neck strength, will be compared between concussed and non-concussed athletes to determine if neck strength can indeed reduce risk of concussion. Neck strength will also be analyzed taking into account concussion severity and duration to find if a strengthening program can provide a protective factor to athletes. The study population will consist of student-athletes, ages 12–23, from local high schools and colleges. These athletes are involved in a range of both contact and non-contact sports.
Background The manual extraction of valuable data from electronic medical records is cumbersome, error-prone, and inconsistent. By automating extraction in conjunction with standardized terminology, the quality and consistency of data utilized for research and clinical purposes would be substantially improved. Here, we set out to develop and validate a framework to extract pertinent clinical conditions for traumatic brain injury (TBI) from computed tomography (CT) reports. Methods We developed tbiExtractor, which extends pyConTextNLP, a regular expression algorithm using negation detection and contextual features, to create a framework for extracting TBI common data elements from radiology reports. The algorithm inputs radiology reports and outputs a structured summary containing 27 clinical findings with their respective annotations. Development and validation of the algorithm was completed using two physician annotators as the gold standard. Results tbiExtractor displayed high sensitivity (0.92-0.94) and specificity (0.99) when compared to the gold standard. The algorithm also demonstrated a high equivalence (94.6%) with the annotators. A majority of clinical findings (85%) had minimal errors (F1 Score � 0.80). When compared to annotators, tbiExtractor extracted information in significantly less time (0.3 sec vs 1.7 min per report).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.