Introduction: The Walch classification has been widely accepted and further developed as a method to characterize glenohumeral arthritis. However, many studies have reported low and inconsistent measures of the reliability of the Walch classification. The purpose of this study was to review the literature on the reliability of the Walch classification and characterize how imaging modality and classification modifications affect reliability.
Methods: A systematic review of publications that included reliability of the Walch classification reported through intraobserver and interobserver kappa values was conducted. A search in January 2021 and repeated in July 2023 used the terms ["Imaging" OR "radiography" OR "CT" OR "MRI"] AND ["Walch classification"] AND ["Glenoid arthritis" OR "Shoulder arthritis"]. All clinical studies from database inception to July 2023 that evaluated the Walch or modified Walch classification's intraobserver and/or interobserver reliability were included. Cadaveric studies and studies that involved subjects with previous arthroplasty, shoulder débridement, glenoid reaming, interposition arthroplasty, and latarjet or bankart procedure were excluded. Articles were categorized by imaging modality and classification modification.
Results: Thirteen articles met all inclusion criteria. Three involved the evaluation of plain radiographs, 10 used CT, two used three-dimensional (3D) CT, and four used magnetic resonance imaging. Nine studies involved the original Walch classification system, five involved a simplified version, and four involved the modified Walch. Six studies examined the reliability of raters of varying experience levels with none reporting consistent differences based on experience. Overall intraobserver reliability of the Walch classifications ranged from 0.34 to 0.92, and interobserver reliability ranged from 0.132 to 0.703. No consistent trends were observed in the effect of the imaging modalities or classification modifications on reliability.
Discussion: The reliability of the Walch classification remains inconsistent, despite modification and imaging advances. Consideration of the limitations of the classification system is important when using it for treatment or prognostic purposes.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.5435/JAAOS-D-22-01086 | DOI Listing |
Bone Joint J
December 2024
New England Baptist Hospital, Boston, Massachusetts, USA.
Shoulder Elbow
August 2024
Biomedical Engineering, Penn State University, University Park, PA, USA.
Background: This retrospective study investigated associations of rotator cuff muscle atrophy (MA) and fatty infiltration (FI) with glenoid morphology.
Methods: Patients with primary glenohumeral osteoarthritis who presented to Penn State Bone and Joint Institute's orthopaedic clinic from September 2002 to December 2019 as total shoulder arthroplasty (TSA) candidates were evaluated. MA was determined by the cross-sectional area of each rotator cuff muscle on pre-operative MR and CT scans.
J Biol Rhythms
December 2024
Department of Mathematics, University of Michigan, Ann Arbor, Michigan, USA.
Wearable devices have become commonplace tools for tracking behavioral and physiological parameters in real-world settings. Nonetheless, the practical utility of these data for clinical and research applications, such as sleep analysis, is hindered by their noisy, large-scale, and multidimensional characteristics. Here, we develop a neural network algorithm that predicts sleep stages by tracking topological features (TFs) of wearable data and model-driven clock proxies (CPs) reflecting the circadian propensity for sleep.
View Article and Find Full Text PDFShoulder Elbow
July 2024
Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, NY, USA.
Background: Indications for reverse total shoulder arthroplasty(rTSA) continue to expand making it challenging to predict whether patients will benefit more from anatomic TSA(aTSA) or rTSA. The purpose of this study was to determine which factors differ between aTSA and rTSA patients that achieve meaningful outcomes and may influence surgical indication.
Methods: Random Forest dimensionality reduction was applied to reduce 23 features into a model optimizing substantial clinical benefit (SCB) prediction of the American Shoulder and Elbow Surgeon score using 1117 consecutive patients with 2-year follow up.
J Shoulder Elbow Surg
September 2024
Faculty of Behavioural and Movement Sciences, Vrije Universiteit van Amsterdam, Amsterdam, The Netherlands.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!