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相关概念视频

Assessment of the Mouth01:26

Assessment of the Mouth

1.6K
A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.
1.6K

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相关实验视频

Updated: Apr 24, 2026

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
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Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

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自我报告的牙周炎:一个基于多民族社区的验证研究

Charlene E Goh1, Jacob Ren Jie Chew1, Clement Lai1

  • 1Faculty of Dentistry, National University of Singapore, Singapore.

Journal of dentistry
|December 10, 2025
PubMed
概括
此摘要是机器生成的。

自我报告的牙周测量,包括松动的牙,有效地在不同亚洲人群中识别严重的牙周炎. 机器学习模型增强了对公共卫生监测的查潜力.

关键词:
流行病学 流行病学口腔健康 口腔健康牙周病是指牙周病的发生.自我报告 自我报告敏感度 敏感度 敏感度具体性 具体性 具体性验证 验证 验证 验证

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Last Updated: Apr 24, 2026

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Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
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科学领域:

  • 口腔流行病学的口腔流行病学.
  • 公共卫生监督是对公共卫生的监督.
  • 诊断准确性的研究研究.

背景情况:

  • 牙周炎在亚洲非常普遍,需要可访问的查方法.
  • 临床牙周检查对于大规模研究来说是不切实际的.
  • 在亚洲人口中,自我报告的措施和社会人口统计因素的验证是有限的.

研究的目的:

  • 在多民族的新加坡人群中临床验证自我报告的牙周指标.
  • 为检测牙周炎开发结合社会人口统计数据和自我报告措施的预测模型.
  • 评估机器学习模型在预测牙周炎方面的性能.

主要方法:

  • 从426名接受牙周检查并填写CDC-AAP自我报告问卷的参与者的横截面数据分析.
  • 牙周炎的定义使用2012年CDC-AAP病例定义.
  • 应用多变量逻辑回归,AUC分析和五种机器学习模型用于预测建模.

主要成果:

  • 一个结合自我报告的松动牙,年龄和种族的模型显示严重牙周炎的良好歧视 (AUC=0.76).
  • 机器学习模型实现了类似的AUC (0.67-0.76),但具有较高的特异性和较低的灵敏性.
  • 轻度,中度和严重的牙周炎分别在16.4%,42.5%和18.1%的参与者中存在.

结论:

  • 自我报告的问题,特别是关于松动的牙,对于在这个多民族群体中检测严重的牙周炎是有价值的.
  • 机器学习模型显示可扩展的,数据驱动的牙周查有希望,尽管需要更大的数据集来提高概括性.
  • 将自我报告数据与人口变量相结合,为牙周炎监测提供了一个实用的方法.