Objective： To assess whether these characteristics of less misclassification and greater area under receiver operator characteristic （ROC） curve of the new injury severity score （NISS） are better than the injury ...Objective： To assess whether these characteristics of less misclassification and greater area under receiver operator characteristic （ROC） curve of the new injury severity score （NISS） are better than the injury severity score （ISS） as applying it to our multiple trauma patients registered into the emergency intensive care unit （EICU）. Methods： This was a retrospective review of registry data from 2 286 multiple trauma patients consecutively registered into the EICU from January 1,1997 to December 31, 2006 in the Second Affiliated Hospital, Medical School of Zhejiang University in China. Comparisons between ISS and NISS were made using misclassification rates, ROC curve analysis, and the H-L statistics by univariate and multivariate logistic progression model. Results： Among the 2 286 patients, 176 （7.7%） were excluded because of deaths on arrival or patients less than 16 years of age. The study population therefore comprised 2 1 10 patients. Mean EICU length of stay （LOS） was 7.8 days ± 2.4 days. Compared with the blunt injury group, the penetrating injury group had a higher percentage of male, lower mean EICU LOS and age. The most frequently injured body regions were extremities and head/neck, followed by thorax, face and abdomen in the blunt injury group; whereas, thorax and abdomen were more frequently seen in the penetrating injury group. The minimum misclassification rate for NISS was slightly less than ISS in all groups （4.01% versus 4.49%）. However, NISS had more tendency to misclassify in the penetrating injury group. This, we noted, was attributed mainly to a higher false-positive rate （21.04% versus 15.55% for IS S, t=-3.310, P〈0.001）, resulting in an overall misclassification rate of 23.57% for NISS versus 18.79% for ISS （t=3.290, P〈0.001）. In the whole sample, NISS presented equivalent discrimination （area under ROC curve： NISS=0.938 versus ISS=0.943）. The H-L statistics showed poorer calibration （48.64 versus 32.11, t=3.305, P〈0.001） in the 展开更多
目的观察并比较火针和电针治疗肾虚髓亏型膝骨关节炎的临床疗效。方法将110例肾虚髓亏型膝骨关节炎患者随机分为两组,火针组56例,电针组54例。火针组采用毫针针刺和火针点刺治疗,电针组采用电针治疗。两组均隔日治疗1次,4星期为1个疗程...目的观察并比较火针和电针治疗肾虚髓亏型膝骨关节炎的临床疗效。方法将110例肾虚髓亏型膝骨关节炎患者随机分为两组,火针组56例,电针组54例。火针组采用毫针针刺和火针点刺治疗,电针组采用电针治疗。两组均隔日治疗1次,4星期为1个疗程,共治疗1个疗程。观察两组治疗前后视觉模拟评分(visual analogue scale,VAS)和西安大略和麦克马斯特大学骨关节炎指数(western Ontario and Mc Master universities osteoarthritis index,WOMAC)量表各项评分变化,并比较两组的临床疗效。结果两组治疗后VAS评分及WOMAC量表各项评分较同组治疗前均有显著性降低(P<0.01)。两组治疗后VAS评分和WOMAC量表各项评分比较,均无统计学差异(P>0.05)。两组治疗后4星期VAS评分和WOMAC量表各项评分均较治疗后进一步降低(P<0.01)。电针组治疗后8星期VAS评分和WOMAC量表疼痛、僵硬评分与治疗后4星期比较,差异有统计学意义(P<0.01),电针组治疗后8星期WOMAC量表疼痛、僵硬、关节功能评分与治疗后比较,差异有统计学意义(P<0.01);火针组治疗后8星期VAS评分和WOMAC量表疼痛、僵硬、关节功能评分与治疗后比较,差异有统计学意义(P<0.01)。火针组治疗后4星期VAS评分和WOMAC量表疼痛、僵硬及关节功能评分均明显低于电针组(P<0.05,P<0.01),治疗后8星期VAS评分和WOMAC量表疼痛、僵硬评分仍显著低于电针组(P<0.01)。火针组总有效率为94.6%,电针组为90.7%,两组比较差异无统计学意义(P>0.05)。结论毫针针刺和火针点刺与电针均能明显改善肾虚髓亏型膝骨关节炎疼痛、僵硬和关节功能,两者近期疗效相当,毫针针刺和火针点刺在远期疗效方面优于电针。展开更多
This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context,...This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context, the resource allocation has to achieve the effective-efficient-equality aim and tries to balance the different desires of two management layers： central manager and each sector. In mathematical programming context, to solve the resource allocation asks for introducing many optimization techniques such as multiple-objective programming and goal programming. We construct an algorithm framework by using comprehensive DEA tools including CCR, BCC models, inverse DEA model, the most compromising common weights analysis model, and extra resource allocation algorithm. Returns to scale characteristic is put major place for analyzing DMUs＇ scale economies and used to select DMU candidates before resource allocation. By combining extra resource allocation algorithm with scale economies target, we propose a resource allocation solution, which can achieve the effective-efficient-equality target and also provide information for future resource allocation. Many numerical examples are discussed in this paper, which also verify our work.展开更多
文摘Objective： To assess whether these characteristics of less misclassification and greater area under receiver operator characteristic （ROC） curve of the new injury severity score （NISS） are better than the injury severity score （ISS） as applying it to our multiple trauma patients registered into the emergency intensive care unit （EICU）. Methods： This was a retrospective review of registry data from 2 286 multiple trauma patients consecutively registered into the EICU from January 1,1997 to December 31, 2006 in the Second Affiliated Hospital, Medical School of Zhejiang University in China. Comparisons between ISS and NISS were made using misclassification rates, ROC curve analysis, and the H-L statistics by univariate and multivariate logistic progression model. Results： Among the 2 286 patients, 176 （7.7%） were excluded because of deaths on arrival or patients less than 16 years of age. The study population therefore comprised 2 1 10 patients. Mean EICU length of stay （LOS） was 7.8 days ± 2.4 days. Compared with the blunt injury group, the penetrating injury group had a higher percentage of male, lower mean EICU LOS and age. The most frequently injured body regions were extremities and head/neck, followed by thorax, face and abdomen in the blunt injury group; whereas, thorax and abdomen were more frequently seen in the penetrating injury group. The minimum misclassification rate for NISS was slightly less than ISS in all groups （4.01% versus 4.49%）. However, NISS had more tendency to misclassify in the penetrating injury group. This, we noted, was attributed mainly to a higher false-positive rate （21.04% versus 15.55% for IS S, t=-3.310, P〈0.001）, resulting in an overall misclassification rate of 23.57% for NISS versus 18.79% for ISS （t=3.290, P〈0.001）. In the whole sample, NISS presented equivalent discrimination （area under ROC curve： NISS=0.938 versus ISS=0.943）. The H-L statistics showed poorer calibration （48.64 versus 32.11, t=3.305, P〈0.001） in the
文摘目的观察并比较火针和电针治疗肾虚髓亏型膝骨关节炎的临床疗效。方法将110例肾虚髓亏型膝骨关节炎患者随机分为两组,火针组56例,电针组54例。火针组采用毫针针刺和火针点刺治疗,电针组采用电针治疗。两组均隔日治疗1次,4星期为1个疗程,共治疗1个疗程。观察两组治疗前后视觉模拟评分(visual analogue scale,VAS)和西安大略和麦克马斯特大学骨关节炎指数(western Ontario and Mc Master universities osteoarthritis index,WOMAC)量表各项评分变化,并比较两组的临床疗效。结果两组治疗后VAS评分及WOMAC量表各项评分较同组治疗前均有显著性降低(P<0.01)。两组治疗后VAS评分和WOMAC量表各项评分比较,均无统计学差异(P>0.05)。两组治疗后4星期VAS评分和WOMAC量表各项评分均较治疗后进一步降低(P<0.01)。电针组治疗后8星期VAS评分和WOMAC量表疼痛、僵硬评分与治疗后4星期比较,差异有统计学意义(P<0.01),电针组治疗后8星期WOMAC量表疼痛、僵硬、关节功能评分与治疗后比较,差异有统计学意义(P<0.01);火针组治疗后8星期VAS评分和WOMAC量表疼痛、僵硬、关节功能评分与治疗后比较,差异有统计学意义(P<0.01)。火针组治疗后4星期VAS评分和WOMAC量表疼痛、僵硬及关节功能评分均明显低于电针组(P<0.05,P<0.01),治疗后8星期VAS评分和WOMAC量表疼痛、僵硬评分仍显著低于电针组(P<0.01)。火针组总有效率为94.6%,电针组为90.7%,两组比较差异无统计学意义(P>0.05)。结论毫针针刺和火针点刺与电针均能明显改善肾虚髓亏型膝骨关节炎疼痛、僵硬和关节功能,两者近期疗效相当,毫针针刺和火针点刺在远期疗效方面优于电针。
基金This research is supported by 973 Program under Grant No.2006CB701306
文摘This paper is concerned with the resource allocation problem based on data envelopment analysis （DEA） which is generally found in practice such as in public services and in production process. In management context, the resource allocation has to achieve the effective-efficient-equality aim and tries to balance the different desires of two management layers： central manager and each sector. In mathematical programming context, to solve the resource allocation asks for introducing many optimization techniques such as multiple-objective programming and goal programming. We construct an algorithm framework by using comprehensive DEA tools including CCR, BCC models, inverse DEA model, the most compromising common weights analysis model, and extra resource allocation algorithm. Returns to scale characteristic is put major place for analyzing DMUs＇ scale economies and used to select DMU candidates before resource allocation. By combining extra resource allocation algorithm with scale economies target, we propose a resource allocation solution, which can achieve the effective-efficient-equality target and also provide information for future resource allocation. Many numerical examples are discussed in this paper, which also verify our work.
