目的:探討鎖骨接骨板這一技術在治療鎖骨中段骨折中的應用及其臨床效果。方法: 通過系統回顧2005年5月至2008年6月我院收治的30例鎖骨中段骨折患者，其中男性24例，女性6例；年齡范圍從12歲到63歲，平均年齡為34歲，行手術時間為受傷后3～5天，經患側刀砍形切口切開復位，予鎖骨接骨板內固定，術后2周內予頸腕吊帶懸吊，同時進行聳肩訓練。術后2周后開始肩關節不持重功能鍛煉。結果:30例患者手術均獲成功，術后隨訪時間為4～12個月（平均隨訪時間6.5個月），所有患者局部無疼痛，行X線檢查顯示均為解剖骨性愈合，外觀無畸形，18例患者一年后取出內固定，無再骨折發生，患者能接受切口線狀疤痕，肩關節活動度:前屈平均155°，外展平均160°。結論:切開復位鎖骨接骨板內固定鎖骨中段骨折是一種較好的治療方法，值得推薦。
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, na?ve 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 na?ve 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.