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使用施密特-劳恩斯宾方程与西加德方程估计的六西格玛的影响在西班牙I型EQA计划中
Fernando Marqués-García1,2, Elisabeth González-Lao2,3, Xavier Tejedor-Ganduxé1,2
1Department of Clinical Biochemistry, Laboratori Clínic Metropolitana Nord, Germans Trias i Pujol University Hospital, Badalona, Barcelona, Spain.
Advances in laboratory medicine
|September 22, 2025
概括
与施密特-劳恩斯宾方程一起的Z转换方法在外部质量保证计划中提供了比传统的Westgard方程更强大的西格玛值估计,通过准确地调整偏差.
科学领域:
- 临床化学 临床化学
- 质量管理系统 质量管理系统
- 统计过程控制 统计过程控制
背景情况:
- 六西格玛方法 (SM) 对于使用每百万个机会中的缺陷 (DPMO) 来衡量过程性能至关重要.
- 传统的SM在外部质量保证 (EQA) 计划中的实施是有限的,阻碍了有效的评估.
- 韦斯特加德方程 (WM) 间接计算DPMO,而Z转换方法与施密特-劳恩斯宾方程 (S-LM) 提供直接计算.
研究的目的:
- 为了比较来自Westgard方程 (WM) 的六西格玛方法 (SM) 值与Z转换+施密特-劳恩斯宾方法 (S-LM) 值.
- 在EQA程序中评估这些方法对西格玛值 (SV) 估计的影响.
主要方法:
- 西格玛值 (SV) 是使用I型EQA程序 (SCR-EQA-SEQCML) 的数据计算的.
- 采用了两种方法:传统的韦斯特加德方程 (WM) 和Z转换+施密特-劳恩斯宾方法 (S-LM).
- 通过这两种方法获得的SV对949个数据点进行了比较分析.
主要成果:
- 发现,与S-LM方法相比,Westgard方程 (WM) 低估了西格玛值 (SV).
- 这种低估是一致的,无论异常值被删除,表明一个偏差问题而不是不准确.
- 与S-LM相比,WM的SV值较低 (1.9没有异常值,2.9除了异常值).
结论:
- 在S-LM方法调整偏差,防止不切实际的负西格玛值 (SVs).
- 与WM相比,S-LM在异常值方面表现出更强的稳定性,这导致了EQA中更可靠的SV估计.
- 这种提高了SV估计的准确性,提高了EQA程序中方法/系统性能的精确评估和分类.
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