Westgard James O
Clin Chem Lab Med. 2016 Feb;54(2):223-33. doi: 10.1515/cclm-2015-0710.
The 2014 Milan Conference "Defining analytical performance goals 15 years after the Stockholm Conference" initiated a new discussion of issues concerning goals for precision, trueness or bias, total analytical error (TAE), and measurement uncertainty (MU). Goal-setting models are critical for analytical quality management, along with error models, quality-assessment models, quality-planning models, as well as comprehensive models for quality management systems. There are also critical underlying issues, such as an emphasis on MU to the possible exclusion of TAE and a corresponding preference for separate precision and bias goals instead of a combined total error goal. This opinion recommends careful consideration of the differences in the concepts of accuracy and traceability and the appropriateness of different measures, particularly TAE as a measure of accuracy and MU as a measure of traceability. TAE is essential to manage quality within a medical laboratory and MU and trueness are essential to achieve comparability of results across laboratories. With this perspective, laboratory scientists can better understand the many measures and models needed for analytical quality management and assess their usefulness for practical applications in medical laboratories.
2014年米兰会议“斯德哥尔摩会议15年后的分析性能目标界定”开启了关于精密度、准确性或偏差、总分析误差（TAE）和测量不确定度（MU）目标相关问题的新一轮讨论。目标设定模型对于分析质量管理至关重要，与误差模型、质量评估模型、质量规划模型以及质量管理体系的综合模型一样重要。还存在一些关键的潜在问题，比如强调MU可能会排除TAE，以及相应地倾向于单独的精密度和偏差目标而非综合的总误差目标。本观点建议仔细考虑准确性和可追溯性概念的差异以及不同测量方法的适用性，特别是将TAE作为准确性的测量方法，将MU作为可追溯性的测量方法。TAE对于医学实验室内部的质量控制至关重要，而MU和准确性对于实现不同实验室结果的可比性至关重要。从这个角度来看，实验室科学家能够更好地理解分析质量管理所需的众多测量方法和模型，并评估它们在医学实验室实际应用中的效用。
