[Recent comparative statistical correlation studies on predicting the space requirement of the cuspid and bicuspid region using multiple regression comparisons].
The purpose of the investigation was to develop multiple regression equations to calculate the space requirements in the canine and premolar regions. The investigation was based on study models from a group of 63 (36 males and 27 females) patients with an ideal Angle class I occlusion. The mesiodistal width of unerupted incisors and the buccolingual and mesiodistal width of first molars were used as independent variables. A test group of 64 study models (32 males and 32 females) from orthodontically treated patients was used to represent the malocclusions typically present in an orthodontic clinic or practice. The space requirement can be predicted from three variables in both upper and lower jaws. The inclusion of more variables does not significantly improve the prediction. The correlation coefficient for the buccolingual width of the first lower molar was significantly higher than that for the mesiodistal width. The combination of 22, 32 and 36 (buccolingual) achieved the highest correlation coefficient in both groups. Further, if at the time of analyses, one of the canines or premolars (33, 34 or 24) has erupted their width may be included as an additional independent variable. The correlation and regression coefficients for males and females showed no significant difference.
Gross
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Hasund
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D001641:Bicuspid / Q000033:anatomy & histology; D002648:Child; D003481:Cuspid / Q000033:anatomy & histology; D003818:Dentition, Mixed; D005260:Female; D006801:Humans; D008297:Male; D008310:Malocclusion / Q000175:diagnosis; D003765:Models, Dental; D009811:Odontometry / Q000706:statistics & numerical data*; D009970:Orthodontics / Q000706:statistics & numerical data*; D011379:Prognosis; D012044:Regression Analysis; D012727:Sex Characteristics; D014078:Tooth Eruption
Neuere vergleichende korrelationsstatistische Untersuchungen zur Vorhersage des Platzbedarfs in den Stützzonen durch multiple Regressionsgleichungen.