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Linear regression modeling to compare fluoride release profiles of various restorative materials.

作者信息

Can-Karabulut Deniz C, Batmaz Inci, Solak Hikmet, Taştekin Mustafa

机构信息

Department of Restorative Dentistry, Faculty of Dentistry, University of Ankara, Ankara 06500, Turkey.

出版信息

Dent Mater. 2007 Sep;23(9):1057-65. doi: 10.1016/j.dental.2006.06.046. Epub 2006 Dec 11.

Abstract

OBJECTIVES

The aim of this study was to compare the released fluoride profiles of various restorative materials by using linear regression analysis.

METHODS

Specimens were prepared using a cylindrical Teflon mold with a height of 2 mm and a radius of 8 mm. After being prepared, specimens were immediately placed into artificial saliva which was replaced at various times during 6 weeks. These released intrinsic fluoride amounts were measured by using an ion selective electrode. Then, data obtained cumulatively were statistically analyzed, and the released profiles were compared.

RESULTS

It was observed that the materials released fluoride at different levels of concentration and the largest fluoride release was obtained from the conventional glass ionomer cement. This was followed by resin modified glass ionomer cement, polyacid modified composite resin, and fluoride releasing composite resin, respectively. Although the released fluoride amounts of the materials were different, their release profiles were found to be similar in that the release was initially fast and then it became steady as time passed.

SIGNIFICANCE

The statistical modeling of the release profiles helps to compare the fluoride release behavior of materials and also to predict fluoride release amounts for the future. In literature, for these purposes, separate nonlinear statistical models have extensively been utilized. However, the single linear statistical modeling approach has numerous advantages such as providing estimators having good statistical properties, exact results, precise inference and simplicity in calculation. Therefore, this study was conducted to introduce the use of single linear regression modeling to compare release profiles statistically.

摘要

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