Suppr超能文献

一个预测蒙特利尔5岁儿童龋齿增量的多变量模型。

A multivariate model to predict caries increment in Montreal children aged 5 years.

作者信息

Demers M, Brodeur J M, Mouton C, Simard P L, Trahan L, Veilleux G

机构信息

School of Dentistry, Laval University, Québec, Canada.

出版信息

Community Dent Health. 1992 Sep;9(3):273-81.

PMID:1451000
Abstract

A study was carried out in Montreal (Canada) to predict caries development over the period of one year in primary teeth of kindergarten children (mean age 5 years 8 months +/- 4 months) living in a non-fluoridated area. The 302 children were examined at school on two occasions, one year apart. At the first examination selected predictors were collected: caries experience, salivary S. mutans and lactobacilli, buffer capacity, debris index, parents' education, fluoride consumption and family structure (one or two parents). Regression analysis was performed to select the significant factors. A total of 143 children developed new caries over the study period; the mean increment for the whole group was 2.1 dmfs. Sensitivity (Sn) and specificity (Sp) were calculated for each predictor and for the final model. The best model comprised only two factors, caries experience and lactobacillus. This could identify 81.8 per cent of children who would develop new caries during the next 12 months (Sn) and 77.4 per cent of those who would not (Sp). Among the single predictors caries experience alone reached 78.3 per cent for sensitivity and 77.4 per cent for specificity. None of the other predictors, except parents' education, was very good at predicting caries increment over one year.

摘要

在加拿大蒙特利尔开展了一项研究,以预测生活在无氟地区的幼儿园儿童(平均年龄5岁8个月,上下浮动4个月)乳牙在一年时间内的龋齿发展情况。这302名儿童在学校接受了两次检查,间隔一年。在第一次检查时收集了选定的预测因素:龋齿经历、唾液中的变形链球菌和乳酸菌、缓冲能力、牙垢指数、父母教育程度、氟摄入量以及家庭结构(单亲或双亲)。进行回归分析以选择显著因素。在研究期间共有143名儿童出现了新的龋齿;整个组的平均增量为2.1 dmfs。计算了每个预测因素以及最终模型的敏感性(Sn)和特异性(Sp)。最佳模型仅包含两个因素,即龋齿经历和乳酸菌。这可以识别出在接下来12个月内会出现新龋齿的儿童中的81.8%(Sn)以及不会出现新龋齿的儿童中的77.4%(Sp)。在单一预测因素中,仅龋齿经历的敏感性达到78.3%,特异性达到77.4%。除了父母教育程度外,其他预测因素在预测一年中的龋齿增量方面都不太理想。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验