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https://www.selleckchem.com/products/PD-0332991.html 96 seconds); however, this difference was not significant (p = .53). Experts' median total OSCE score (14) was significantly higher (p = .03) than novices' (12), supporting the model's construct validity. Participants agreed on the face and content validity of the model by grading all survey questions greater than 7 on a 10-point Likert-type scale. In summary, we successfully developed a 3D printed model of an equine cervical articular process joint, partially demonstrated the construct validity of the model, and proved the face and content validity of this new training tool.Introduction To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) technologies need to be understood to assist the therapist in obtaining better insights about impairments and level of activity and participation in persons with stroke during rehabilitation. Methods This scoping review maps the use of AI for the functional evaluation of persons with stroke; the context involves any setting of rehabilitation. Data were extracted from CENTRAL, MEDLINE, EMBASE, LILACS, CINAHL, PEDRO Web of Science, IEEE Xplore, AAAI Publications, ACM Digital Library, MathSciNet, and arXiv up to January 2021. The data obtained from the literature review were summarized in a single dataset in which each reference paper was considered as an instance, and the study characteristics were considered as attributes. The attributes used for the multiple correspondence analysis were publication year, study type, sample size, age, stroke phase, stroke type, functional status, AI type, and AI function. Results Forty-four studies were included. The analysis showed that spasticity analysis based on ML techniques was used for the cases of stroke with moderate functional status. The techniques of deep learning and pressure sensors were used for gait analysis. Machine learning techniques and algorithms

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