Object: Although pain relief is a primary goal of a total knee arthroplasty (TKA) and partial knee arthroplasty (PKA), a significant number of arthroplasty patients experience unexplained pain. This study attempts to ...Object: Although pain relief is a primary goal of a total knee arthroplasty (TKA) and partial knee arthroplasty (PKA), a significant number of arthroplasty patients experience unexplained pain. This study attempts to determine preoperative or intraoperative factors that may predict pain after knee arthroplasty. Methods: 2585 primary TKAs and 423 PKAs were performed between 1993 and 2013. Infections, loosening, and revision arthroplasty were excluded. Knee Society scores, demographics, component sizes, pre- and postoperative alignment, treatment of the posterior cruciate ligament, range of motion, and diagnosis were analyzed. Statistical analysis utilized repeated measures ANOVA. Results: Significant predictors of increased pain after TKA were pre-operative varus alignment >5° (p = 0.0042), postoperative flexion range of motion 5° (p = 0.0006), small tibial component sizes (p = 0.0080), excision of the posterior cruciate ligament (p = 0.0259), and diagnosis as osteonecrosis (p = 0.0077). Factors not associated with pain included age, body mass index, gender, postoperative alignment and bone quality. For PKA, age was the only factor associated with pain. Conclusions: Six factors were found to be relevant to postoperative pain in TKA. For PKA, reported pain is not associated with any of the same factors, but is associated with age. Surgeons should be aware of these risk factors as we continue to understand pain after total and partial knee arthroplasty.展开更多
本文在对顾客满意度模型及其估计方法PLS(Partial Least Square)进行简要讨论的基础上,详细研究了顾客满意度模型PLS估计方法需要的样本量,并针对中国顾客满意度研究的实际企业顾客满意度模型数据,给出了顾客满意度模型的样本量要求的建...本文在对顾客满意度模型及其估计方法PLS(Partial Least Square)进行简要讨论的基础上,详细研究了顾客满意度模型PLS估计方法需要的样本量,并针对中国顾客满意度研究的实际企业顾客满意度模型数据,给出了顾客满意度模型的样本量要求的建议,对顾客满意度实践有指导意义。展开更多
为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,...为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。展开更多
文摘Object: Although pain relief is a primary goal of a total knee arthroplasty (TKA) and partial knee arthroplasty (PKA), a significant number of arthroplasty patients experience unexplained pain. This study attempts to determine preoperative or intraoperative factors that may predict pain after knee arthroplasty. Methods: 2585 primary TKAs and 423 PKAs were performed between 1993 and 2013. Infections, loosening, and revision arthroplasty were excluded. Knee Society scores, demographics, component sizes, pre- and postoperative alignment, treatment of the posterior cruciate ligament, range of motion, and diagnosis were analyzed. Statistical analysis utilized repeated measures ANOVA. Results: Significant predictors of increased pain after TKA were pre-operative varus alignment >5° (p = 0.0042), postoperative flexion range of motion 5° (p = 0.0006), small tibial component sizes (p = 0.0080), excision of the posterior cruciate ligament (p = 0.0259), and diagnosis as osteonecrosis (p = 0.0077). Factors not associated with pain included age, body mass index, gender, postoperative alignment and bone quality. For PKA, age was the only factor associated with pain. Conclusions: Six factors were found to be relevant to postoperative pain in TKA. For PKA, reported pain is not associated with any of the same factors, but is associated with age. Surgeons should be aware of these risk factors as we continue to understand pain after total and partial knee arthroplasty.
文摘为提高超短期风速预测的可靠性和准确性,将被预测地点(本地)周边测风塔风速风向等当前和最近历史观测值作为基础数据,采用空间相关性来预测本地的未来风速。首先,依据风向和风速的延迟相关性,挑选出上游测风塔。之后,结合最优延迟时间,利用各上游和本地最近的风速观测值来训练预测模型。最后,将各上游风速的当前观测值输入模型,即可得到本地的风速预测值。以偏最小二乘回归(partial least squares regression,PLSR)为主要模型,并采用线性回归(linear regression,LR)、最小二乘支持向量回归等模型进行对照。以冬季风时期的荷兰Huibertgat和天津为被预测地点,进行了PLSR、LR预测误差与模型阶数、样本容量之间关系的数值实验。研究表明,在冬季风时期,当样本容量达到一定程度后,预测误差的变化对阶数、样本容量和模型的类型均不再敏感。这表明空间相关性是一种较为可靠的超短期风速预测方法。
