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The use of both internal thoracic arteries for coronary revascularization increases the estimate of post-operative lower limb ischemia in patients with peripheral artery disease

机译:用于冠状动脉血管内的内部胸动脉的使用增加了外周血疾病患者术后下肢缺血的估计

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摘要

a. Optimize plot. The plot is a graphical display of the balance criteria as a function of the GBM iteration. The graph demonstrates that a favorable balance was achieved with 1000 iterations and that such a balance was worse with a higher number of iterations that, thus were unnecessary. b. Overlap Assessment. The figure presents two sets of box plots of PS distributions. An overlap between the groups is desirable meaning that there are no values of the pretreatment variables that occur only in one of the treatment conditions. However, there are no specific rules for what constitutes sufficient overlap so, in doubtful cases the combination of the overlap plot and the balance table are used. In our model there is a good overlap between the two boxplots. c. Standardized Effect Size plot. It assesses the balance of pretreatment variables before and after weighting. It shows the maximum pairwise ASMDs. The ASMDs cutoff for defining unbalanced variables was 0.20. The light blue line represents pretreatment covariates for which the maximum pairwise ASMD reduced after weighting. The red lines mean the pretreatment covariates for which the maximum pairwise ASMD increased after weighting. A good balance is obtained when, after weighting in the majority of variables the ASMDs are < 0.20 and there is a prevalence of light blue lines. In our model, after weighting the pretreatment variables, the balance showed excellent results with all ASMD values being well below 0.2. d. Quantile-quantile (Q-Q) plot. This plot also assesses the balance of pretreatment variables. In the plot, the Kolmogorov-Smirnov p-value is plotted against the rank of p-value for pretreatment variables. Along a 45-degree fitting line, open symbols represent weighted covariates and solid symbols represent unweighted covariates. A good balance is obtained when open symbols lie close, below or above, the 45-degree line. After weighting, in our model, all covariates were well above the reference line
机译:一种。优化图。该曲线是作为GBM迭代的函数的平衡标准的图形显示。该图表明,使用1000次迭代实现了有利的平衡,并且这种平衡与较高的迭代次数较差,因此不必要。湾重叠评估。该图呈现了两组PS分布的盒子图。组之间的重叠是理想的,意味着在一个治疗条件之一中没有出现的预处理变量的值。但是,没有具体规则是什么构成足够重叠,因此,在令人怀疑的情况下,使用重叠绘图和平衡表的组合。在我们的模型中,两个盒子之间存在良好的重叠。 C。标准化效果大小图。它评估了在加权之前和之后的预处理变量的平衡。它显示了最大的成对ASMD。用于定义不平衡变量的ASMD截止为0.20。浅蓝色线代表预处理协变量,在加权后减少的最大成对ASMD。红线意味着在加权后的最大成对ASMD增加的预处理协变量。当在大多数变量中加权后,获得良好的平衡,ASMDS为<0.20,并且存在浅蓝色线的患病率。在我们的模型中,在加权预处理变量后,平衡显示出优异的结果,所有ASMD值远低于0.2。天。定量定位(Q-Q)图。该曲线还评估预处理变量的平衡。在图中,Kolmogorov-Smirnov P值符合预处理变量的p值等级。沿着45度配合线,开放符号代表加权协变量,实体符号代表未加权的协变量。当打开符号在近距离,低于或更高,45度线时,获得了良好的平衡。在加权后,在我们的模型中,所有协变量都远高于参考线

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