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儿童龋齿高增量的牙科预测因素

Dental predictors of high caries increment in children.

作者信息

Steiner M, Helfenstein U, Marthaler T M

机构信息

Department of Preventive Dentistry, Periodontology and Cariology, University of Zürich, Switzerland.

出版信息

J Dent Res. 1992 Dec;71(12):1926-33. doi: 10.1177/00220345920710121401.

DOI:10.1177/00220345920710121401
PMID:1452896
Abstract

A comprehensive set of dental variables was investigated to find the "best" combination of predictors for high caries increment in 7/8-year-old and 10/11-year-old children. Four populations with widely different caries prevalence were studied. Logistic regression analysis supplied multiple-input models by stepwise selection of predictors. A "low number of sound primary molars" was the best and most consistent predictor of high caries increment. The second best predictors were "high numbers of pre-cavity lesions on permanent first molars" (discolored pits and fissures in the younger age group and white spots on the smooth parts of buccolingual surfaces in the older age group). Inclusion of radiological variables did not substantially increase the quality of prediction. For practical application, models with various multiple inputs selected by stepwise procedures were compared with "fixed" three-input models. These three-input models resulted in predictive quality nearly equal to those of the multiple models. Traditional one-input models, containing DMFT or dmft, were inferior to the three-input models, particularly in the older age class. The lower the caries prevalence of the source data, the better was the prediction. As a summary measure characterizing the predictive performance of a model, we used the index "area under the receiver operating characteristic curve" A. For the 1984 data and the three-input models, the area was approximately 80%, and for the 1972 data, the area was 65-70%.

摘要

研究了一套全面的牙齿变量,以找出7/8岁和10/11岁儿童高龋齿增量的预测因素的“最佳”组合。研究了四个龋齿患病率差异很大的人群。逻辑回归分析通过逐步选择预测因素提供了多输入模型。“健全乳牙数量少”是高龋齿增量的最佳且最一致的预测因素。第二好的预测因素是“恒牙第一磨牙上龋前病变数量多”(较年轻年龄组中变色的窝沟以及较年长年龄组中颊舌面光滑部分的白斑)。纳入放射学变量并未显著提高预测质量。为了实际应用,将通过逐步程序选择的各种多输入模型与“固定”三输入模型进行了比较。这些三输入模型的预测质量几乎与多模型相同。包含DMFT或dmft的传统单输入模型不如三输入模型,尤其是在较年长年龄组中。源数据的龋齿患病率越低,预测效果越好。作为表征模型预测性能的汇总指标,我们使用了“受试者工作特征曲线下面积”指数A。对于1984年的数据和三输入模型,该面积约为80%,对于1972年的数据,该面积为65 - 70%。

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PeerJ. 2019 May 31;7:e6997. doi: 10.7717/peerj.6997. eCollection 2019.
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