RESUMO
INTRODUCTION: The objective of this study was to assess the reproducibility of cervical vertebral maturation (CVM) method based on the type of radiographic image and the level of experience and level of training of the evaluator. METHODS: Ten evaluators (5 orthodontic residents and 5 faculty members) were randomly divided into 2 groups: trained and untrained. All participants evaluated 80 radiographic images previously acquired in 4 different formats: (1) 2-dimensional (2D) digital (2D-digital), (2) 2D digitized hard copy from the Iowa Facial Growth Study (American Association of Orthodontists Foundation Craniofacial Growth Legacy Collection), (3) 2D digital reconstructed from a 3-dimensional (3D) radiograph (2D-from 3D), and (4) 3D cone-beam computerized tomographic (3D-CBCT) images. Agreement among evaluators on the morphology of the cervical vertebrae (CV) and the CVM stage of each radiographic image was assessed using Randolph's kappa statistic and Kendall's W coefficient of concordance. RESULTS: Interobserver agreement on the determination of a curvature on the inferior border of the CV was substantial to perfect, whereas agreement on shape was fair to moderate. Overall, the level training in all image types, except 3D-CBCTs, but not the level of experience affected the agreement for shape and curvature of the CVs. Interobserver agreement on CVM staging for all combined images was substantial at 0.72. Faculty had a higher level of agreement than residents except for 2D-digital and 3D-CBCT images, whereas trained evaluators had an overall higher level of agreement than untrained evaluators except for 3D-CBCT images. CONCLUSIONS: Interobserver agreement in determining CVM stage was substantial for all images evaluated; experience and training resulted in higher level of agreement for some image types. The 3D-CBCT images did not provide increased interobserver agreement over current 2D-digital lateral cephalograms in determining CVM staging or shape of the CV. The highest agreement in CVM staging was obtained on 2D-digital lateral cephalograms with training.
