Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons · 2012
A more accurate method of predicting soft tissue changes after mandibular setback surgery.
Сравнительное исследованиеСтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Suh Hee-Yeon, Lee Shin-Jae, Lee Yun-Sik, Donatelli Richard E, Wheeler Timothy T, Kim Soo-Hwan, Eo Soo-Heang, Seo Byoung-Moo
Аннотация
PURPOSE: To propose a more accurate method to predict the soft tissue changes after orthognathic surgery.
PATIENTS AND METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
PATIENTS AND METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
Предметные рубрики MeSH
AdolescentAdultAlgorithmsAnatomic LandmarksCephalometryChinFaceFemaleFollow-Up StudiesForecastingGenioplastyHumansImage Processing, Computer-AssistedLeast-Squares AnalysisLipMaleMalocclusion, Angle Class IIIMandibleMandibular OsteotomyModels, StatisticalNoseOrthognathic Surgical ProceduresOsteotomy, Sagittal Split RamusPrognathismReproducibility of ResultsSella TurcicaVertical DimensionYoung Adult