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As techniques for equine biomechanical research have become more accessible and affordable, the literature published in this area has exploded. Literature reviews have become more popular of late and, more specifically, several literature reviews in areas related to equine biomechanics have been published. A scoping review is a relatively new approach, where a general aim is to map the body of literature on a topic area, accounting for criteria for inclusion and exclusion. However, problems can arise both in performing the review and in critiquing the findings. In this manuscript, the authors repeat a published scoping review of equine biomechanics aiming to map 'the existing literature in the field of equine movement analysis'. The search criteria from the previous study were reviewed and the performance of the search criteria was iteratively studied to find as many relevant papers as possible. The results yielded 77% more publications than the original review mainly as a consequence of not limiting the search strategy to papers including "equine" or "horse" in the title. The importance of using appropriate and inclusive search terms is highlighted together with evaluating the findings within the context of the discipline and time frame of the review.
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http://dx.doi.org/10.1016/j.jevs.2022.103920 | DOI Listing |
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
JMIR Ment Health
September 2025
Department of Psychology, University of California, Los Angeles, 1285 Franz Hall, Box 951563, Los Angeles, CA, 90095, United States, 1 3107941262.
Background: Youth mental health issues have been recognized as a pressing crisis in the United States in recent years. Effective, evidence-based mental health research and interventions require access to integrated datasets that consolidate diverse and fragmented data sources. However, researchers face challenges due to the lack of centralized, publicly available datasets, limiting the potential for comprehensive analysis and data-driven decision-making.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Institute for Health Care Management and Research, University of Duisburg-Essen, Essen, Germany.
Background: Mental and behavioral disorders affect approximately 28% of the adult population in Germany per year, with treatment being provided through a diverse health care system. Yet there are access and capacity problems in outpatient mental health care. One innovation that could help reduce these barriers and improve the current state of care is the use of mobile health (mHealth) apps, known in Germany as Digitale Gesundheitsanwendungen (DiGA).
View Article and Find Full Text PDFJ Speech Lang Hear Res
September 2025
School of Health Sciences, College of Health and Medicine, University of Tasmania, Launceston, Australia.
Purpose: The purpose of this scoping review was to map and summarize currently available evidence about optimal dosage of recasting for developmental language disorder (DLD).
Method: A systematic search of databases was completed, including Web of Science, Medline via OvidSP, PsycINFO, Cochrane Library, and Scopus. The inclusion criteria comprised the involvement of DLD intervention, the use of recasting alone or predominantly, and systematic manipulation of one or more dosage characteristics of recasting with the remaining parameters consistent across groups.
JBJS Rev
September 2025
Department of Epidemiology & Biostatistics, Schulich School of Medicine & Dentistry, Western University, London, Ontario, Canada.
» There is wide variability in the measurement tools used to assess socioeconomic deprivation status (SDS) in orthopaedic trauma research, including single-item, multi-item, and area-based indices.» Area-based SDS measures are commonly used because they can be readily linked to geographic identifiers in administrative data; however, they are limited by ecological fallacy and may misclassify individual-level socioeconomic status.» The lack of standardization in SDS measurement limits comparability across studies, highlighting the need for core measurement domains to support equity-focused research.
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