The aim of the present paper was to improve and expand research with a larger number of children from various European countries and to provide a common formula useful for all these countries. Orthopantomographs taken from 2,652 European Caucasian children (1,382 boys, 1,270 girls) aged between 4 and 16 years were analyzed. The children came from Croatia, Germany, Kosovo, Italy, Slovenia, Spain, and the UK. Following the pilot study, subjects' age was modeled as a function of gender (g), morphological variables (predictors)×5(second premolar), s (sum of normalized open apices) N 0, and the first-order interaction between s and N0. The results showed that all these variables contributed significantly to the fit, so that all were included in the regression model, yielding the following linear regression formula: Age=8.387+0.282 g-1.692×5+0.835 N0-0.116 s-0.139 s×N0, where g is a variable, 1 for males and 0 for females. The equation explained 86.1% (R2=0.861) of total deviance. The median of the residuals (=observed age minus predicted age) was -0.114 years, with (RefB.2) interquartile range=1.22 years. © 2007 Springer-Verlag.

Age estimation in children by measurement of open apices in teeth: A European formula

Cameriere R.;
2007-01-01

Abstract

The aim of the present paper was to improve and expand research with a larger number of children from various European countries and to provide a common formula useful for all these countries. Orthopantomographs taken from 2,652 European Caucasian children (1,382 boys, 1,270 girls) aged between 4 and 16 years were analyzed. The children came from Croatia, Germany, Kosovo, Italy, Slovenia, Spain, and the UK. Following the pilot study, subjects' age was modeled as a function of gender (g), morphological variables (predictors)×5(second premolar), s (sum of normalized open apices) N 0, and the first-order interaction between s and N0. The results showed that all these variables contributed significantly to the fit, so that all were included in the regression model, yielding the following linear regression formula: Age=8.387+0.282 g-1.692×5+0.835 N0-0.116 s-0.139 s×N0, where g is a variable, 1 for males and 0 for females. The equation explained 86.1% (R2=0.861) of total deviance. The median of the residuals (=observed age minus predicted age) was -0.114 years, with (RefB.2) interquartile range=1.22 years. © 2007 Springer-Verlag.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11695/130408
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