Suh Hee-Yeon, Lee Shin-Jae, Lee Yun-Sik, Donatelli Richard E, Wheeler Timothy T, Kim Soo-Hwan, Eo Soo-Heang, Seo Byoung-Moo
Department of Orthodontics, Seoul National University School of Dentistry and Dental Research Institute, Seoul, Korea.
J Oral Maxillofac Surg. 2012 Oct;70(10):e553-62. doi: 10.1016/j.joms.2012.06.187.
To propose a more accurate method to predict the soft tissue changes after orthognathic surgery.
The subjects included 69 patients who had undergone surgical correction of Class III mandibular prognathism by mandibular setback. Two multivariate methods of forming prediction equations were examined using 134 predictor and 36 soft tissue response variables: the ordinary least-squares (OLS) and the partial least-squares (PLS) methods. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a 10-fold cross-validation method was used.
The multivariate PLS method showed significantly better predictive performance than the conventional OLS method. The bias pattern was more favorable and the absolute prediction accuracy was significantly better with the PLS method than with the OLS method.
The multivariate PLS method was more satisfactory than the conventional OLS method in accurately predicting the soft tissue profile change after Class III mandibular setback surgery.
提出一种更准确的方法来预测正颌手术后的软组织变化。
研究对象包括69例因下颌后缩接受III类下颌前突手术矫正的患者。使用134个预测变量和36个软组织反应变量，检验了两种形成预测方程的多变量方法：普通最小二乘法（OLS）和偏最小二乘法（PLS）。拟合方程后，计算偏差和平均绝对预测误差。为评估预测方程的预测性能，采用了10折交叉验证法。
多变量PLS方法的预测性能明显优于传统的OLS方法。PLS方法的偏差模式更有利，绝对预测准确性明显优于OLS方法。
在准确预测III类下颌后缩手术后的软组织轮廓变化方面，多变量PLS方法比传统的OLS方法更令人满意。
