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      The Australian Traumatic Brain Injury Initiative: Statement of Working Principles and Rapid Review of Methods to Define Data Dictionaries for Neurological Conditions

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          Abstract

          The Australian Traumatic Brain Injury Initiative (AUS-TBI) aims to develop a health informatics approach to collect data predictive of outcomes for persons with moderate-severe TBI across Australia. Central to this approach is a data dictionary; however, no systematic reviews of methods to define and develop data dictionaries exist to-date. This rapid systematic review aimed to identify and characterize methods for designing data dictionaries to collect outcomes or variables in persons with neurological conditions. Database searches were conducted from inception through October 2021. Records were screened in two stages against set criteria to identify methods to define data dictionaries for neurological conditions (International Classification of Diseases, 11th Revision: 08, 22, and 23). Standardized data were extracted. Processes were checked at each stage by independent review of a random 25% of records. Consensus was reached through discussion where necessary. Thirty-nine initiatives were identified across 29 neurological conditions. No single established or recommended method for defining a data dictionary was identified. Nine initiatives conducted systematic reviews to collate information before implementing a consensus process. Thirty-seven initiatives consulted with end-users. Methods of consultation were “roundtable” discussion ( n = 30); with facilitation ( n = 16); that was iterative ( n = 27); and frequently conducted in-person ( n = 27). Researcher stakeholders were involved in all initiatives and clinicians in 25. Importantly, only six initiatives involved persons with lived experience of TBI and four involved carers. Methods for defining data dictionaries were variable and reporting is sparse. Our findings are instructive for AUS-TBI and can be used to further development of methods for defining data dictionaries.

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          Rayyan—a web and mobile app for systematic reviews

          Background Synthesis of multiple randomized controlled trials (RCTs) in a systematic review can summarize the effects of individual outcomes and provide numerical answers about the effectiveness of interventions. Filtering of searches is time consuming, and no single method fulfills the principal requirements of speed with accuracy. Automation of systematic reviews is driven by a necessity to expedite the availability of current best evidence for policy and clinical decision-making. We developed Rayyan (http://rayyan.qcri.org), a free web and mobile app, that helps expedite the initial screening of abstracts and titles using a process of semi-automation while incorporating a high level of usability. For the beta testing phase, we used two published Cochrane reviews in which included studies had been selected manually. Their searches, with 1030 records and 273 records, were uploaded to Rayyan. Different features of Rayyan were tested using these two reviews. We also conducted a survey of Rayyan’s users and collected feedback through a built-in feature. Results Pilot testing of Rayyan focused on usability, accuracy against manual methods, and the added value of the prediction feature. The “taster” review (273 records) allowed a quick overview of Rayyan for early comments on usability. The second review (1030 records) required several iterations to identify the previously identified 11 trials. The “suggestions” and “hints,” based on the “prediction model,” appeared as testing progressed beyond five included studies. Post rollout user experiences and a reflexive response by the developers enabled real-time modifications and improvements. The survey respondents reported 40% average time savings when using Rayyan compared to others tools, with 34% of the respondents reporting more than 50% time savings. In addition, around 75% of the respondents mentioned that screening and labeling studies as well as collaborating on reviews to be the two most important features of Rayyan. As of November 2016, Rayyan users exceed 2000 from over 60 countries conducting hundreds of reviews totaling more than 1.6M citations. Feedback from users, obtained mostly through the app web site and a recent survey, has highlighted the ease in exploration of searches, the time saved, and simplicity in sharing and comparing include-exclude decisions. The strongest features of the app, identified and reported in user feedback, were its ability to help in screening and collaboration as well as the time savings it affords to users. Conclusions Rayyan is responsive and intuitive in use with significant potential to lighten the load of reviewers.
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            Welcome to the Tidyverse

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              Diagnosis and management of dementia with Lewy bodies

              The Dementia with Lewy Bodies (DLB) Consortium has refined its recommendations about the clinical and pathologic diagnosis of DLB, updating the previous report, which has been in widespread use for the last decade. The revised DLB consensus criteria now distinguish clearly between clinical features and diagnostic biomarkers, and give guidance about optimal methods to establish and interpret these. Substantial new information has been incorporated about previously reported aspects of DLB, with increased diagnostic weighting given to REM sleep behavior disorder and 123iodine-metaiodobenzylguanidine (MIBG) myocardial scintigraphy. The diagnostic role of other neuroimaging, electrophysiologic, and laboratory investigations is also described. Minor modifications to pathologic methods and criteria are recommended to take account of Alzheimer disease neuropathologic change, to add previously omitted Lewy-related pathology categories, and to include assessments for substantia nigra neuronal loss. Recommendations about clinical management are largely based upon expert opinion since randomized controlled trials in DLB are few. Substantial progress has been made since the previous report in the detection and recognition of DLB as a common and important clinical disorder. During that period it has been incorporated into DSM-5, as major neurocognitive disorder with Lewy bodies. There remains a pressing need to understand the underlying neurobiology and pathophysiology of DLB, to develop and deliver clinical trials with both symptomatic and disease-modifying agents, and to help patients and carers worldwide to inform themselves about the disease, its prognosis, best available treatments, ongoing research, and how to get adequate support.
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                Author and article information

                Journal
                Neurotrauma Rep
                Neurotrauma Rep
                neur
                Neurotrauma Reports
                Mary Ann Liebert, Inc., publishers (140 Huguenot Street, 3rd Floor New Rochelle, NY 10801 USA )
                2689-288X
                April 2024
                2024
                April 2024
                : 5
                : 1
                : 424-447
                Affiliations
                [ 1 ]Curtin Health Innovation Research Institute, Faculty of Health Sciences, Curtin University, Bentley, Western Australia, Australia.
                [ 2 ]Perron Institute for Neurological and Translational Science, Nedlands, Western Australia, Australia.
                [ 3 ]Centre for Pain IMPACT, Neuroscience Research Australia, Sydney, New South Wales, Australia.
                [ 4 ]School of Health Sciences, University of Notre Dame Australia, Fremantle, Western Australia, Australia.
                [ 5 ]School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia.
                [ 6 ]Monash-Epworth Rehabilitation Research Centre, Epworth Healthcare, Melbourne, Victoria, Australia.
                [ 7 ]Department of Neuroscience, Central Clinical School, Monash University, Melbourne, Victoria, Australia.
                [ 8 ]Alfred Health, Melbourne, Victoria, Australia.
                [ 9 ]National Trauma Research Institute, Melbourne, Victoria, Australia.
                [ 10 ]School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
                [ 11 ]Emergency and Trauma Centre, The Alfred Hospital, Melbourne, Victoria, Australia.
                [ 12 ]Department of Intensive Care and Hyperbaric Medicine, The Alfred Hospital, Melbourne, Victoria, Australia.
                [ 13 ]Brain Injury Australia, Sydney, New South Wales, Australia.
                [ 14 ]Health Data Research UK, Swansea University Medical School, Swansea University, Singleton Park, United Kingdom.
                Author notes
                [ ** ]

                The Australian TBI Initiative Investigators are included at the end of the article.

                [*] [ * ]Address correspondence to: Melinda Fitzgerald, PhD, Curtin University Neuroscience Laboratory, Ralph and Patricia Sarich Neuroscience Research Institute, Level 3, 8 Verdun Street, Nedlands, WA 6009, Australia. lindy.fitzgerald@ 123456curtin.edu.au
                Article
                10.1089/neur.2023.0116
                10.1089/neur.2023.0116
                11040195
                38660461
                3264397b-b11e-400a-af92-fc994d65dca0
                © Matthew K. Bagg et al., 2024; Published by Mary Ann Liebert, Inc.

                This Open Access article is distributed under the terms of the Creative Commons License [CC-BY] ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Page count
                Figures: 1, Tables: 4, References: 139, Pages: 24
                Categories
                Original Article

                brain injuries,common data elements,neurology,systematic review [publication type],traumatic

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