
Early prospective evaluation for self-correction of deciduous anterior crossbite can enable identification of patients who require early treatment as well as those who do not. The purpose of the present study was to generate an algorithm that can be used to predict self-correction in the transitional dentition in 3-year-old subjects. The subjects were divided into 2 groups. One group comprised 22 subjects whose anterior crossbite self-corrected during the transitional stage (hereafter referred to as group N). The other group was comprised of 22 subjects whose anterior crossbite persisted during the transitional dentition (hereafter referred to as group R). All subjects were examined using lateral cephalometric radiography in order to evaluate differences in occlusion. Fifteen measurements were used for the evaluation. For each measurement, the variance ratio and the difference in the population mean between groups N and R were tested and t-values were derived. Based on the Student's t-test results, only measurements that had statistically significant differences (P < .05) were extracted. Predictor variables that had a partial F value of 5 or greater were selected for stepwise discriminant analysis, and the following equation was obtained: deciduous indicator (DI) = -0.58(cranial length anterior) + 1.31(posterior facial height) - 0.76(porion location) - 2.02(Wits appraisal) - 70.28. The lower the DI value (negative), the higher the probability that the crossbite will self-correct at the transitional dentition. On the other hand, a high (positive) discriminant score strongly suggests that the subject requires treatment in the primary dentition. The result of this analysis showed that the apparent error rate was 95.46% and the Maharanobis' generalized distance was 8.99.
Cephalometry, Remission, Spontaneous, Discriminant Analysis, Prognosis, Decision Support Techniques, Dentition, Permanent, Child, Preschool, Humans, Female, Tooth, Deciduous, Algorithms, Malocclusion, Forecasting
Cephalometry, Remission, Spontaneous, Discriminant Analysis, Prognosis, Decision Support Techniques, Dentition, Permanent, Child, Preschool, Humans, Female, Tooth, Deciduous, Algorithms, Malocclusion, Forecasting
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