American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2008
Prediction of soft-tissue changes after mandibular advancement surgery with an equation developed with multivariable regression.
Оценочное исследованиеСтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Kneafsey Louise C, Cunningham Susan J, Petrie Aviva, Hutton Tim J
Аннотация
INTRODUCTION: This was a retrospective cephalometric study in patients undergoing mandibular advancement surgery. Our aim was to provide a more precise estimation of the postsurgical soft-tissue outcomes than can be achieved by using simple ratios of the hard and soft tissues.
METHODS: The lateral cephalograms of 64 patients undergoing mandibular advancement, from before and near the end of treatment, were scanned and digitized with a customized software program. Multivariable regression analyses were used to create prediction equations for soft-tissue changes at pogonion, inferior labial sulcus, labrale inferius, and stomion inferius (all in the horizontal plane).
RESULTS AND CONCLUSIONS: This method of using multiple explanatory variables appears to be useful in the prediction of soft-tissue changes. At least 96% of the variation of each dependent variable was explained by its relationship with the explanatory variables in the relevant multivariable regression equation, and the results appeared to be clinically useful.
METHODS: The lateral cephalograms of 64 patients undergoing mandibular advancement, from before and near the end of treatment, were scanned and digitized with a customized software program. Multivariable regression analyses were used to create prediction equations for soft-tissue changes at pogonion, inferior labial sulcus, labrale inferius, and stomion inferius (all in the horizontal plane).
RESULTS AND CONCLUSIONS: This method of using multiple explanatory variables appears to be useful in the prediction of soft-tissue changes. At least 96% of the variation of each dependent variable was explained by its relationship with the explanatory variables in the relevant multivariable regression equation, and the results appeared to be clinically useful.