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一种使用数学模型预测青春期男孩成年身高的改进方法。

Improved method for predicting adult height of pubertal boys using a mathematical model.

作者信息

Limony Y, Zadik Z, Pic A K, Leiberman E

机构信息

Pediatric Endocrine Unit, Soroka Medical Center, Beer-Sheva, Israel.

出版信息

Horm Res. 1993;40(4):117-22. doi: 10.1159/000183779.

DOI:10.1159/000183779
PMID:8300058
Abstract

Several methods for adult height prediction are currently in use. All are subject to a wide range of error which is thought to result, at least in part, from the use of bone age estimation. Following the suggestion made by Karlberg to predict adult height of pubertal children by the use of the 'Infancy-Childhood-Puberty model' (ICP), growth data of 39 normal boys who were followed from infancy until adult height was attained were reviewed. Use of the ICP model alone and without bone age resulted in more accurate predictions of adult height than those made by the methods which required bone age determination, the Bayley-Pinneu (BP) and the Tanner-Whitehouse methods (TW). The absolute error of prediction was 3.4 cm as compared to 5.3 (BP) and 4.9 cm (TW) (p < 0.05) and maximal range of error was 10 cm as compared to 22 (BP) and 21 cm (TW) (p < 0.05). Finally, based on the ICP model, a new equation which incorporates paternal height was been derived. This equation, termed ICP-New (ICPN), resulted in even better accuracy: absolute error of prediction was 2.3 cm as compared to 3.4 cm (ICP) and maximal range of error was 6 cm as compared to 10 cm (ICP) (p < 0.05). It is concluded that the ICP model and the ICPN equation may predict the adult height of pubertal boys more accurately than the methods which use bone age.

摘要

目前有几种预测成人身高的方法正在使用。所有这些方法都存在很大的误差范围,人们认为至少部分误差是由于使用骨龄估计造成的。按照卡尔伯格的建议,通过使用“婴儿期 - 儿童期 - 青春期模型”(ICP)来预测青春期儿童的成人身高,我们回顾了39名从婴儿期一直追踪到达到成人身高的正常男孩的生长数据。单独使用ICP模型且不考虑骨龄,比那些需要确定骨龄的方法(贝利 - 平纽方法(BP)和坦纳 - 怀特豪斯方法(TW))能更准确地预测成人身高。预测的绝对误差为3.4厘米,相比之下,BP方法为5.3厘米,TW方法为4.9厘米(p < 0.05);误差的最大范围为10厘米,相比之下,BP方法为22厘米,TW方法为21厘米(p < 0.05)。最后,基于ICP模型,推导出了一个纳入父亲身高的新方程。这个方程称为ICP - 新方程(ICPN),其准确性更高:预测的绝对误差为2.

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