Reliability of the Sigmoid Notch Classification of the Distal Radioulnar Joint

Abstract

Background The Tolat sigmoid notch classification is a commonly used classification to characterize the distal radioulnar joint (DRUJ). This classification was based on a limited assessment of the entire joint, which may lead to inaccuracies in sigmoid notch evaluation.

Questions/Purposes The purpose of this study is to assess the reliability of the Tolat classification for sigmoid notch characterization.

Methods The sigmoid notch of 52 models of cadaveric forearms was assessed by applying the Tolat classification to the three-dimensional (3D) modeled notch and then slices at the start of the notch (0 mm) and 4 mm more proximal. The inter- and intrarater agreement was assessed using Cohen's and Fleiss' kappa statistic.

Results Agreement between iterations regardless of slices or surgeons/radiologists was moderate. Intrarater agreement between pairs of slices (0 vs 4 mm, 0 mm vs 3D, 4 mm vs 3D) was moderate, whereas agreement between all slices was slight. Agreement between surgeons and between radiologists was moderate, while agreement across all raters and slices was fair. Models described as “other” were more consistent in 3D classifications and were commonly classified as a reverse ski slope.

Conclusions Classification using the Tolat scheme is fair to moderate at best. Classification of the sigmoid notch using an axial view of the distal radius may not accurately reflect the anatomy throughout the notch.

Clinical Relevance The Tolat classification supplies a limited analysis of the sigmoid notch, and does not represent a comprehensive evaluation of the entire joint. Future classification systems should characterize the entire sigmoid notch.

Keywords

sigmoid notch - Tolat - distal radioulnar joint

This work was completed at the Department of Orthopaedics, University of British Columbia, Vancouver, BC.

Publikationsverlauf

Eingereicht: 26. Januar 2022

Angenommen: 11. Oktober 2022

Artikel online veröffentlicht:
18. November 2022

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