目的探讨肛肠手术后慢性疼痛(CPSP)的危险因素。方法收集2018年8月至2019年10月择期行肛肠手术746例患者资料,并记录人口学特征、合并症、术前疼痛情况、围术期情况等。通过电话随访术后1、3个月时的疼痛情况,根据术后是否发生CPSP将患...目的探讨肛肠手术后慢性疼痛(CPSP)的危险因素。方法收集2018年8月至2019年10月择期行肛肠手术746例患者资料,并记录人口学特征、合并症、术前疼痛情况、围术期情况等。通过电话随访术后1、3个月时的疼痛情况,根据术后是否发生CPSP将患者分为两组:CPSP组和非CPSP组。采用多因素Logistic回归分析CPSP的危险因素。结果有37例(4.96%)患者发生CPSP。与非CPSP组比较,CPSP组术前合并疼痛、高血压、贫血、术后7 d VAS疼痛评分>3分、术后发生出血、睡眠障碍和便秘的比例明显升高(P<0.05)。多因素Logistic回归分析显示,术前疼痛(OR=3.022,P=0.013)、术前贫血(OR=2.235,P=0.017)、术后出血(OR=3.511,P=0.034)、术后睡眠障碍(OR=2.345,P=0.003)以及术后7 d VAS疼痛评分>3分(OR=4.323,P=0.006)是发生肛肠手术后CPSP的危险因素。结论肛肠手术CPSP发生率较低,术前疼痛、术前贫血、术后出血、术后睡眠障碍以及术后7 d VAS疼痛评分>3分是发生肛肠手术CPSP的危险因素。展开更多
Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient tempor...Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient temporal information.In order to highlight the time information,we propose an efficient deep learning architecture for violence detection based on temporal attention mechanism,which utilizes pre-trained MobileNetV3,convolutional LSTM and temporal attention block Temporal Adaptive(TA).TA block can focus on further refining temporal information from spatial information extracted from backbone.Experimental results show the proposed model is validated on three publicly datasets:Hockey Fight,Movies,and RWF-2000 datasets.展开更多
文摘目的探讨肛肠手术后慢性疼痛(CPSP)的危险因素。方法收集2018年8月至2019年10月择期行肛肠手术746例患者资料,并记录人口学特征、合并症、术前疼痛情况、围术期情况等。通过电话随访术后1、3个月时的疼痛情况,根据术后是否发生CPSP将患者分为两组:CPSP组和非CPSP组。采用多因素Logistic回归分析CPSP的危险因素。结果有37例(4.96%)患者发生CPSP。与非CPSP组比较,CPSP组术前合并疼痛、高血压、贫血、术后7 d VAS疼痛评分>3分、术后发生出血、睡眠障碍和便秘的比例明显升高(P<0.05)。多因素Logistic回归分析显示,术前疼痛(OR=3.022,P=0.013)、术前贫血(OR=2.235,P=0.017)、术后出血(OR=3.511,P=0.034)、术后睡眠障碍(OR=2.345,P=0.003)以及术后7 d VAS疼痛评分>3分(OR=4.323,P=0.006)是发生肛肠手术后CPSP的危险因素。结论肛肠手术CPSP发生率较低,术前疼痛、术前贫血、术后出血、术后睡眠障碍以及术后7 d VAS疼痛评分>3分是发生肛肠手术CPSP的危险因素。
文摘Violence detection is very important for public safety.However,violence detection is not an easy task.Because recognizing violence in surveillance video requires not only spatial information but also sufficient temporal information.In order to highlight the time information,we propose an efficient deep learning architecture for violence detection based on temporal attention mechanism,which utilizes pre-trained MobileNetV3,convolutional LSTM and temporal attention block Temporal Adaptive(TA).TA block can focus on further refining temporal information from spatial information extracted from backbone.Experimental results show the proposed model is validated on three publicly datasets:Hockey Fight,Movies,and RWF-2000 datasets.
