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EnglishThe 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) X5 (second premolar), s (sum of normalized open apices) N0, 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 X5+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 interquartile range=1.22 years.
| Titolo: | Age estimation in children by measurement of open apices in teeth: a European formula | |
| Autori: | ||
| Data di pubblicazione: | 2007 | |
| Rivista: | ||
| 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) X5 (second premolar), s (sum of normalized open apices) N0, 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 X5+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 interquartile range=1.22 years. | |
| Handle: | http://hdl.handle.net/11566/35990 | |
| Appare nelle tipologie: | 1.1 Articolo in rivista |
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