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前言有几种个体体质因素是对高空减压病危险性有影响的。本文中作者采用Bayes氏理论来评估低气压情况下减压病的个体危险性因素。方法应用美国国家航空航天局屈肢痛数据库(n=516)的资料,以微气泡Ⅲ级作为阳性,首次检验了用多普勒可测微气泡预测减压病症状的诊断准确性。在参加本试验的一些受试者(n=164)中采用个体体质因素的资料按逻辑斯蒂回归模式估算其先验高空减压病危险性,再利用Bayes氏理论由多普勒检测结果计算个体减压病后
Preface There are several individual physical factors that affect the risk of altitude decompression sickness. In this paper, the author uses Bayes’ theory to evaluate individual risk factors for decompression sickness under low pressure conditions. Methods The data of NASA flexion and leg pain database (n = 516) were used to test the diagnostic accuracy of the decompression sickness by using the microbubbles detected by Doppler. Some subjects who participated in the trial (n = 164) used logistic regression models to estimate the risk of a priori decompression sickness using data of individual constitutional factors and then calculated the results of Doppler detection using Bayes’ theory After the individual decompression sickness