Effects of a finger exercise program on hand function in automobile workers with hand osteoarthritis: A randomized controlled trial.

Hand osteoarthritis reduces a person's ability to perform work activities and return to their occupation. We investigated the effects of a finger exercise program on hand grip strength, pain, physical function, and stiffness in automobile manufacturing workers with hand osteoarthritis. This randomized controlled trial was conducted on 29 subjects. Fifteen experimental subjects received a finger exercise program with paraffin baths, while 14 control subjects received only paraffin baths. Hand grip strength, pain, physical function, and stiffness were assessed at baseline and 8 weeks later. In the experimental group, hand grip strength (P < 0.001) and Australian/Canadian osteoarthritis hand index (AUSCAN) scores (pain, P < 0.001; stiffness, P < 0.001; physical function, P < 0.001) were significantly improved by 3.52 ± 2.03, 21.6 ± 8.3 (pain), 16.8 ± 10.21 (stiffness), and 13.86 ± 4.54 (physical function) compared with preintervention values. In the control group, hand grip strength (P = 0.004) and AUSCAN scores (pain, P < 0.001; stiffness, P = 0.019; physical function, P < 0.001) were significantly improved by 0.57 ± 0.62, 7.85 ± 5.46 (pain) 11.42 ± 7.18 (stiffness), and 10.28 ± 14.41 (physical function) compared with preintervention values. Significant differences between groups were found for postintervention hand grip strength (P = 0.015) and AUSCAN index subscale scores (pain, P < 0.001; physical function, P = 0.020). A combined finger exercise and paraffin bath program is effective in reducing pain, improving physical function, and increasing hand grip strength in workers with hand osteoarthritis.

Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discrimination.

BACKGROUND AND PURPOSE: Risk-stratification models based on patient and disease characteristics are useful for aiding clinical decisions and for comparing the quality of care between different physicians or hospitals. In addition, prediction of mortality is beneficial for optimizing resource utilization. We evaluated the accuracy and discriminating power of the random forest (RF) to predict 30-day mortality of spontaneous intracerebral hemorrhage (SICH). METHODS: We retrospectively studied 423 patients admitted to the Taichung Veterans General Hospital who were diagnosed with spontaneous SICH within 24 h of stroke onset. The initial evaluation data of the patients were used to train the RF model. Areas under the receiver operating characteristic curves (AUC) were used to quantify the predictive performance. The performance of the RF model was compared to that of an artificial neural network (ANN), support vector machine (SVM), logistic regression model, and the ICH score. RESULTS: The RF had an overall accuracy of 78.5% for predicting the mortality of patients with SICH. The sensitivity was 79.0%, and the specificity was 78.4%. The AUCs were as follows: RF, 0.87 (0.84-0.90); ANN, 0.81 (0.77-0.85); SVM, 0.79 (0.75-0.83); logistic regression, 0.78 (0.74-0.82); and ICH score, 0.72 (0.68-0.76). The discriminatory power of RF was superior to that of the other prediction models. CONCLUSIONS: The RF provided the best predictive performance amongst all of the tested models. We believe that the RF is a suitable tool for clinicians to use in predicting the 30-day mortality of patients after SICH.