Reproducibility of the Mayo and Schatzker classification systems in proximal ulna fractures.
Classifications
Elbow
Mayo
Proximal ulna
Reliability
Schatzker
Journal
Orthopaedics & traumatology, surgery & research : OTSR
ISSN: 1877-0568
Titre abrégé: Orthop Traumatol Surg Res
Pays: France
ID NLM: 101494830
Informations de publication
Date de publication:
08 Dec 2023
08 Dec 2023
Historique:
received:
16
02
2023
revised:
18
05
2023
accepted:
02
06
2023
pubmed:
10
12
2023
medline:
10
12
2023
entrez:
9
12
2023
Statut:
aheadofprint
Résumé
A fracture classification system should provide a reliable and reproducible means of communication between different parties. It should be logical and understandable, with few categories to memorize. The aim of this study was to determine the intra- and interobserver reliability of the Schatzker and Mayo classification systems for the assessment of proximal ulna fractures. Intra- and interobserver reliability studies were conducted on 39 X-rays of injured elbows drawn randomly from 74 cases previously used in a series on predictors of ulnohumeral osteoarthritis in proximal ulna fractures. Ten observers independently reviewed these X-rays on 2 separate occasions 3 months apart. The fracture type was assessed according to the Schatzker and Mayo classification systems during each reading session. Cohen's and Fleiss' kappa were used to measure the intra- and interobserver reliability. The Schatzker classification had a fair interobserver reliability for the first (Schatzker R The classification systems for proximal ulna fractures showed poor reproducibility between the different observers since they had low interobserver agreement values. Nevertheless, their use remained reliable since the measured intraobserver agreement value was deemed substantial. IV; retrospective.
Identifiants
pubmed: 38070730
pii: S1877-0568(23)00342-0
doi: 10.1016/j.otsr.2023.103790
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
103790Informations de copyright
Copyright © 2023 Elsevier Masson SAS. All rights reserved.