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ObjectiveTo explore the effect of repetitive peripheral magnetic stimulation on upper limb motor function rehabilitation of stroke patients after contralateral seventh cervical nerve transfer (CC7). MethodsFrom May, 2020, to May, 2022, 34 stroke patients with hemiplegia underwent CC7 in Jing'an District Centre Hospital of Shanghai were randomly divided into control group (n = 17) and observation group (n = 17). Both groups received conventional rehabilitation. The observation group accepted repetitive peripheral magnetic stimulation, and the control group received sham stimulation, for eight weeks. They were assessed with Fugl-Meyer Assessment-Upper Extremities (FMA-UE) and Hua-Shan Grading of Upper Extremity (H-S grading) before and after treatment. ResultsTwo cases dropped down in each group. There was difference in gender between two groups (χ2 = 6.136, P < 0.05). After treatment, the scores of FMA-UE and H-S grading significantly improved in both groups (t > 4.000, P < 0.01), and the improvement was better in the observation group than in the control group (t > 2.362, P < 0.05). ConclusionRepetitive peripheral magnetic stimulation could improve the motor function of upper limb and hand of stroke patients with hemiplegia after CC7.
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BACKGROUND: Fetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction. METHODS: In the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software. RESULTS: We compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908-0.927) and 0.910 (95% CI, 0.894-0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model. CONCLUSIONS: Our model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.
