Ko Sangbong, Choi Wonkee, Chae Seungbum
Daegu Catholic University Medical Center, 33, Duryugongwon-ro 17-gil, Nam-gu, Daegu, 42472, Korea.
Eur Spine J. 2017 Sep;26(9):2290-2296. doi: 10.1007/s00586-017-5187-3. Epub 2017 Jun 13.
The aim is to analyze the agreement between different types of physicians in terms of the inter-observer and intra-observer reliability in addition to the agreement between the experienced and non-experienced physicians with respect to three different classification systems for diagnosis of cervical spinal canal stenosis.
Total nine doctors including experienced group of three doctors and non-experienced group of six doctors classified the patients according to three different classification in an independent, blinded manner using magnetic resonance imaging (MRI) to diagnose cervical canal stenosis. MRI slice included sagittal plane (midline cut) and an image slice from each horizontal plane that penetrated the right center of each disk (C3-4, C4-5, C5-6, and C6-7) was made by PPT format.
For the inter-observer reliability, Vaccaro et al.'s classification system showed the excellent reproducibility, followed by Muhle et al. and Kang et al. All three classification systems showed excellent reproducibility and substantial agreement in terms of the intra-observer reliability.
All three classification systems showed excellent reproducibility and also displayed a substantial agreement. The classification system used by Vaccaro et al. was proven to be a method with substantial agreement both in the experienced group and the non-experienced group. It can be a useful classification system for simplifying communication among all physicians.
除了分析经验丰富和经验不足的医生在三种不同的颈椎管狭窄诊断分类系统方面的一致性外，还要分析不同类型医生在观察者间和观察者内可靠性方面的一致性。
总共九名医生，包括三名经验丰富的医生和六名经验不足的医生，使用磁共振成像（MRI）以独立、盲法的方式根据三种不同的分类对患者进行分类，以诊断颈椎管狭窄。MRI切片包括矢状面（中线切面），并且通过PPT格式制作了从每个穿过每个椎间盘（C3 - 4、C4 - 5、C5 - 6和C6 - 7）右中心的水平面获取的图像切片。
对于观察者间可靠性，Vaccaro等人的分类系统显示出极好的可重复性，其次是Muhle等人和Kang等人的分类系统。就观察者内可靠性而言，所有三种分类系统均显示出极好的可重复性和高度一致性。
所有三种分类系统均显示出极好的可重复性，并且也表现出高度一致性。Vaccaro等人使用的分类系统被证明是一种在经验丰富组和经验不足组中都具有高度一致性的方法。它可以成为一种有助于简化所有医生之间沟通的有用分类系统。
