Background: To provide reference for hand function assessment and treatment effectiveness by measuring changes in muscle strength before and after rehabilitation in patients with deep hand burns.
Methods: Clinical data from 112 patients with deep right hand burns treated at the Third Affiliated Hospital of Inner Mongolia Medical University were collected. Passive Functional Training: hand training was conducted using the continuous passive motion system, once daily for 40 minutes each session. The therapist performed resistance exercises, stretching training, and other motion therapies on the fingers. Specific pressure therapy was applied by the therapist based on the location and shape of the scars. Grip strength and finger to finger strength data of hand burn patients were measured before and after treatment on days 14, 21, 28, 35, 42, 60, 90, and 120 after wound healing. Scores from the Fugl-Meyer hand motor section, Michigan Hand Outcomes Questionnaire (MHQ), Vancouver Scar Scale (VSS), and Visual Analog Scale (VAS) for Pain were recorded before treatment. A two-way ANOVA was employed to evaluate the effects of time and group on rehabilitation outcomes. Pearson and Spearman correlation analysis were employed to explore correlations among variables.
Results: The two-way ANOVA analysis demonstrated significant effects of time, treatment, and their interaction (all P < 0.001). Statistically significant differences in resultant force were observed between pre- and post-treatment at days 21, 28, 35, 42 and 60 ( all P<0.001). The correlation coefficients between pre-treatment resultant force and Fugl-Meyer and MHQ scores were 0.848 and 0.852, respectively. The correlation coefficient between resultant force and grip strength was 0.899.
Conclusions: Measurement of hand muscle strength after wound healing could serve as a critical index for assessing hand function recovery status.
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http://dx.doi.org/10.1016/j.burns.2025.107415 | DOI Listing |
ChatGPT and other artificial intelligence (AI) tools can modify nutritional management in clinical settings. These technologies, based on machine learning and deep learning, enable the identification of risks, the proposal of personalized interventions, and the monitoring of patient progress using data extracted from clinical records. ChatGPT excels in areas such as nutritional assessment by calculating caloric needs and suggesting nutrient-rich foods, and in diagnosis, by identifying nutritional issues with technical terminology.
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Department of Neurology, Charité - Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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Shenzhen Polytechnic University, Shenzhen, 518055, People's Republic of China.
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