Bolin A
Swed Dent J Suppl. 1986;35:1-108.
The progression of proximal alveolar bone loss (ABD index) in an unselected material comprising 406 individuals was analysed by a longitudinal investigation over a period of ten years. In order to minimize the number of measurements and the drop-out of measurements of the difference in alveolar bone height between the two examinations a partial recording system was constructed. A partial index including five sites (12 m, 11 m, 33 d, 31 d, 41 m) gave high correlation to a total recording (r = 0.96). The best alternative in periapical radiographs to measuring the alveolar bone height in relation to the root length was to measure it in relation to the tooth length. This second method does not necessitate identification of the cemento-enamel junction. The mean alveolar bone difference was 5.5 per cent of the mean root length, which corresponds to an average bone loss of 0.09 mm per year. Eighteen variables were analysed as predictors in stepwise multiple regression analyses. The dependent variable was the "ABD index". Four predictors reached significance at 1 per cent level in the multivariate analysis, the alveolar bone loss 1970 ("ABL index 1970"), "Age", "Number of lost teeth" and "Russell's Periodontal Index" ("PI"). The coefficient of determination (R2) was 0.40. In a selected part of the material, consisting of individuals with at least 20 remaining teeth, the stepwise multiple regression analysis was repeated with the same 18 predictors. On this occasion two predictors reached significance at 1 per cent level, "PI" and "Smoking", and these two factors showed an interaction.
通过对406名个体组成的未选样本进行为期十年的纵向研究,分析了近端牙槽骨丧失(ABD指数)的进展情况。为了尽量减少测量次数以及两次检查之间牙槽骨高度差异测量的缺失,构建了一个部分记录系统。一个包含五个位点(12 m、11 m、33 d、31 d、41 m)的部分指数与全面记录具有高度相关性(r = 0.96)。在根尖片上,相对于牙根长度测量牙槽骨高度的最佳替代方法是相对于牙长进行测量。第二种方法无需识别牙骨质-釉质界。牙槽骨平均差异为平均牙根长度的5.5%,这相当于每年平均骨丧失0.09毫米。在逐步多元回归分析中,对18个变量作为预测因素进行了分析。因变量是“ABD指数”。在多变量分析中,四个预测因素在1%水平上具有显著性,即1970年牙槽骨丧失(“ABL指数1970”)、“年龄”、“失牙数量”和“罗素牙周指数”(“PI”)。决定系数(R2)为0.40。在该样本中选取的至少保留20颗牙齿的个体部分,使用相同的18个预测因素重复进行逐步多元回归分析。此时,两个预测因素在1%水平上具有显著性,即“PI”和“吸烟”,并且这两个因素存在相互作用。