一种数据驱动的方法从牙科图表中识别了的亚型
Simon Haworth1, Lisa Kastenbom2, Peter Persson3,4
1Bristol Dental School, University of Bristol, Bristol, UK.
Community dentistry and oral epidemiology
|October 22, 2024
概括
来自SKaPa登记册的瑞典牙科数据对于成年人来说是准确的. 隐性类分析 (LCA) 有效地识别出具有明显健康和行为特征的虫子类型.
科学领域:
- 口腔健康研究研究 口腔健康研究
- 公共卫生监督 公共卫生监督
- 生物统计学 生物统计学
背景情况:
- 瑞典牙和牙周炎质量登记册 (SKaPa) 收集成人牙科数据.
- 验证注册表数据和探索先进的分析方法对于公共卫生至关重要.
- 了解病模式可以为有针对性的干预提供信息.
研究的目的:
- 评估SKaPa注册表中成人牙科数据的准确性.
- 为了确定隐性类分析 (LCA) 是否可以根据虫病数据识别不同的患者群体.
- 描述这些已识别的群体的牙科,医疗和行为特征.
主要方法:
- 来自SKaPa的牙科数据与马尔默后代牙科研究 (MODS) 中牙医的临床数据进行了验证.
- 隐性类分析 (LCA) 用于将来自Västerbotten干预计划 (VIP) 的61,984名成年参与者分为五组.
- 牙状况,病史,人体测量和行为数据在LCA衍生类和DMFS排名五分之一之间进行了比较.
主要成果:
- SKaPa牙科数据与参考临床数据有很好的一致性 (类内相关性>0.90).
- 根据DMFS分数,LCA成功地确定了五个不同的参与者类别,范围从10.0到94.4.4.
- 在LCA类和各种健康/行为措施之间发现了显著的关联,LCA捕获的信息在DMFS排名组中并不明显.
结论:
- 在SKaPa注册表中的2年窗口内的成人牙科数据是准确的.
- LCA提供了一种强大的方法来将个人分为虫子类型.
- 这些脏亚型表现出不同的健康和行为特征以及未来的脏进展,突出了LCA在个性化口腔健康方面的潜力.
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