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• W4380996075 endingPage "953" @default.
• W4380996075 startingPage "953" @default.
• W4380996075 abstract "Proprioceptive impairments occur in ~50% of stroke survivors, with 20-40% still impaired six months post-stroke. Early identification of those likely to have persistent impairments is key to personalizing rehabilitation strategies and reducing long-term proprioceptive impairments. In this study, clinical, neuroimaging and robotic measures were used to predict proprioceptive impairments at six months post-stroke on a robotic assessment of proprioception. Clinical assessments, neuroimaging, and a robotic arm position matching (APM) task were performed for 133 stroke participants two weeks post-stroke (12.4 ± 8.4 days). The APM task was also performed six months post-stroke (191.2 ± 18.0 days). Robotics allow more precise measurements of proprioception than clinical assessments. Consequently, an overall APM Task Score was used as ground truth to classify proprioceptive impairments at six months post-stroke. Other APM performance parameters from the two-week assessment were used as predictive features. Clinical assessments included the Thumb Localisation Test (TLT), Behavioural Inattention Test (BIT), Functional Independence Measure (FIM) and demographic information (age, sex and affected arm). Logistic regression classifiers were trained to predict proprioceptive impairments at six months post-stroke using data collected two weeks post-stroke. Models containing robotic features, either alone or in conjunction with clinical and neuroimaging features, had a greater area under the curve (AUC) and lower Akaike Information Criterion (AIC) than models which only contained clinical or neuroimaging features. All models performed similarly with regard to accuracy and F1-score (>70% accuracy). Robotic features were also among the most important when all features were combined into a single model. Predicting long-term proprioceptive impairments, using data collected as early as two weeks post-stroke, is feasible. Identifying those at risk of long-term impairments is an important step towards improving proprioceptive rehabilitation after a stroke." @default.
• W4380996075 created "2023-06-17" @default.
• W4380996075 creator A5005141406 @default.
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• W4380996075 date "2023-06-15" @default.
• W4380996075 modified "2023-09-30" @default.
• W4380996075 title "Clinical, Neuroimaging and Robotic Measures Predict Long-Term Proprioceptive Impairments following Stroke" @default.
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• W4380996075 doi "https://doi.org/10.3390/brainsci13060953" @default.
• W4380996075 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37371431" @default.
• W4380996075 hasPublicationYear "2023" @default.
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