Lateral ankle sprain injury is the most common musculoskeletal injury incurred by individuals who participate in sports and recreational physical activities. Following initial injury, a high proportion of individuals develop long-term injury-associated symptoms and chronic ankle instability. The development of chronic ankle instability is consequent on the interaction of mechanical and sensorimotor insufficiencies/impairments that manifest following acute lateral ankle sprain injury. To reduce the propensity for developing chronic ankle instability, clinical assessments should evaluate whether patients in the acute phase following lateral ankle sprain injury exhibit any mechanical and/or sensorimotor impairments. This modified Delphi study was undertaken under the auspices of the executive committee of the International Ankle Consortium. The primary aim was to develop recommendations, based on expert (n=14) consensus, for structured clinical assessment of acute lateral ankle sprain injuries. After two modified Delphi rounds, consensus was achieved on the clinical assessment of acute lateral ankle sprain injuries. Consensus was reached on a minimum standard clinical diagnostic assessment. Key components of this clinical diagnostic assessment include: establishing the mechanism of injury, as well as the assessment of ankle joint bones and ligaments. Through consensus, the expert panel also developed the International Ankle Consortium Rehabilitation-Oriented ASsessmenT (ROAST). The International Ankle Consortium ROAST will help clinicians identify mechanical and/or sensorimotor impairments that are associated with chronic ankle instability. This consensus statement from the International Ankle Consortium aims to be a key resource for clinicians who regularly assess individuals with acute lateral ankle sprain injuries.
Lateral ankle sprains (LASs) are a common injury sustained by individuals who participate in recreational physical activities and sports. After an LAS, a large proportion of individuals develop long-term symptoms, which contribute to the development of chronic ankle instability (CAI). Due to the prevalence of LASs and the propensity to develop CAI, collective efforts toward reducing the risk of sustaining these injuries should be a priority of the sports medicine and sports physiotherapy communities. The comprehensive injury-causation model was developed to illustrate the interaction of internal and external risk factors in the occurrence of the inciting injury. The ability to mitigate injury risk is contingent on a comprehensive understanding of risk factors for injury. The objective of this current concepts review is to use the comprehensive injury-causation model as a framework to illustrate the risk factors for LAS and CAI based on the literature.
The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to multiple drug repurposing clinical trials that have yielded largely uncertain outcomes. To overcome this challenge, we used IDentif.AI, a platform that pairs experimental validation with artificial intelligence (AI) and digital drug development to rapidly pinpoint unpredictable drug interactions and optimize infectious disease combination therapy design with clinically relevant dosages. IDentif.AI was paired with a 12-drug candidate therapy set representing over 530,000 drug combinations against the SARS-CoV-2 live virus collected from a patient sample. IDentif.AI pinpointed the optimal combination as remdesivir, ritonavir, and lopinavir, which was experimentally validated to mediate a 6.5-fold enhanced efficacy over remdesivir alone. Additionally, it showed hydroxychloroquine and azithromycin to be relatively ineffective. The study was completed within 2 weeks, with a three-order of magnitude reduction in the number of tests needed. IDentif.AI independently mirrored clinical trial outcomes to date without any data from these trials. The robustness of this digital drug development approach paired with in vitro experimentation and AI-driven optimization
Introduction Joint angle measurement is an important objective marker in rehabilitation. Inertial measurement units may provide an accurate and reliable method of joint angle assessment. The objective of this study was to assess whether a single sensor with the application of machine learning algorithms could accurately measure hip and knee joint angle, and investigate the effect of inertial measurement unit orientation algorithms and person-specific variables on accuracy. Methods Fourteen healthy participants completed eight rehabilitation exercises with kinematic data captured by a 3D motion capture system, used as the reference standard, and a wearable inertial measurement unit. Joint angle was calculated from the single inertial measurement unit using four machine learning models, and was compared to the reference standard to evaluate accuracy. Results Average root-mean-squared error for the best performing algorithms across all exercises was 4.81° (SD = 1.89). The use of an inertial measurement unit orientation algorithm as a pre-processing step improved accuracy; however, the addition of person-specific variables increased error with average RMSE 4.99° (SD = 1.83°). Conclusions Hip and knee joint angle can be measured with a good degree of accuracy from a single inertial measurement unit using machine learning. This offers the ability to monitor and record dynamic joint angle with a single sensor outside of the clinic.
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