目的:探讨锁骨接骨板这一技术在治疗锁骨中段骨折中的应用及其临床效果。方法: 通过系统回顾2005年5月至2008年6月我院收治的30例锁骨中段骨折患者，其中男性24例，女性6例；年龄范围从12岁到63岁，平均年龄为34岁，行手术时间为受伤后3～5天，经患侧刀砍形切口切开复位，予锁骨接骨板内固定，术后2周内予颈腕吊带悬吊，同时进行耸肩训练。术后2周后开始肩关节不持重功能锻炼。结果:30例患者手术均获成功，术后随访时间为4～12个月（平均随访时间6.5个月），所有患者局部无疼痛，行X线检查显示均为解剖骨性愈合，外观无畸形，18例患者一年后取出内固定，无再骨折发生，患者能接受切口线状疤痕，肩关节活动度:前屈平均155°，外展平均160°。结论:切开复位锁骨接骨板内固定锁骨中段骨折是一种较好的治疗方法，值得推荐。
Objective To investigate the network reorganization and dynamic brain activity in visuospatial neglect (VSN) patients using resting-state electroencephalography (rEEG), and to develop classification models to facilitate its identification. Methods In this retrospective study, stroke patients admitted to the Department of Rehabilitation, Xuanwu Hospital, Capital Medical University between August 2022 and December 2024 were included and divided into VSN (n=22) and non-VSN (n=21) groups based on paper-and-pencil assessments. A healthy control group (n=20) was also recruited. Microstate segmentation and graph-theoretical analysis were applied to rEEG data to extract microstate parameters and topological network features. Four machine learning models (logistic regression, naïve Bayes, k-nearest neighbors, and decision tree) were built for classification. Results Compared with the non-VSN group, the VSN group showed significantly increased mean duration and time coverage in microstate C, and significantly decreased coverage and occurrence in microstate D. Graph-theoretical analysis revealed higher average clustering coefficients in the VSN group. Degree centrality in the frontal-central regions (C1, CZ) was significantly lower, while that in the parietal-occipital regions (P5, P3, PO7, PO5) was significantly higher than in the non-VSN group. Among the classification models, logistic regression and naïve Bayes models performed best, with the mean duration of microstate C contributing most to classification performance. Conclusions Patients with VSN exhibit distinct alterations in electroencephalography microstate dynamics and functional network topology. Microstate parameters play a crucial role in distinguishing VSN from non-VSN stroke cases, and combining these features with machine learning offers a promising approach for early identification and personalized intervention of VSN.