Introduction: Although there is no consensus in the literature, it is believed that the Soong classification system and fracture pattern are risk factors for plate removal in distal radius fractures.
Hypothesis: The aim of this large-scale study was to evaluate the relationship between Soong classification, fracture pattern, and implant removal in distal radius fractures.
Materials And Methods: We retrospectively evaluated 795 patients who underwent surgery using a volar locking plate for distal radius fractures at our clinic between 2005 and 2022. The patients were divided into two groups: implant removed, and implant retained. The groups were examined for demographic data, follow-up periods, fracture classifications, and radiological parameters. Additionally, the patients were divided into groups and compared according to the Soong classification, which was determined according to implant placement. Indications for implant removal were also included in this study, and their relationships with other parameters were evaluated.
Results: A total of 123 and 672 patients were included in the implant removed and retained groups, respectively. The average age of the implant removed group was significantly lower (p = 0.005). There were no significant differences between the two groups in terms of fracture classification or other radiological parameters. In the implant removed group, the rate of grade 2, according to the Soong classification, was statistically higher than that in the other groups (p = 0.019). Flexor tenosynovitis was the most common reason for implant removal.
Conclusion: The Soong classification system is an important risk factor associated with implant removal. This risk may increase, particularly among young patients. Surgeons should consider placing the distal radius locking plate as proximally as possible to reduce the frequency of implant removal.
Level Of Evidence: III; retrospective comparative study.
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http://dx.doi.org/10.1016/j.otsr.2025.104172 | DOI Listing |
Orthop Traumatol Surg Res
January 2025
Ankara University Faculty of Medicine, Orthopaedics and Traumatology Department, Hand Surgery Unit, Ankara, Turkey.
Introduction: Although there is no consensus in the literature, it is believed that the Soong classification system and fracture pattern are risk factors for plate removal in distal radius fractures.
Hypothesis: The aim of this large-scale study was to evaluate the relationship between Soong classification, fracture pattern, and implant removal in distal radius fractures.
Materials And Methods: We retrospectively evaluated 795 patients who underwent surgery using a volar locking plate for distal radius fractures at our clinic between 2005 and 2022.
Diagnostics (Basel)
December 2024
Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, NUHS Tower Block Level 10, 1E Kent Ridge Road, Singapore 119228, Singapore.
Background: Endoscopic assessment for the diagnosis of gastric cancer is limited by interoperator variability and lack of real-time capability. Recently, Raman spectroscopy-based artificial intelligence (AI) has been proposed as a solution to overcome these limitations.
Objective: To compare the performance of the AI-enabled Raman spectroscopy with that of high-definition white light endoscopy (HD-WLE) for the risk classification of gastric lesions.
Orthop Traumatol Surg Res
December 2024
Service de Chirurgie Orthopédique et Traumatologique, Hôpital de la Cavale Blanche, Boulevard Tanguy Prigent, Brest 29200, France; Université de Bretagne Occidentale, UBO, Brest 29200, France; LaTIM, INSERM, UMR 1101, SFR IBSAM, Avenue Foch, Brest 29200, France. Electronic address:
Arch Bone Jt Surg
January 2024
Department of Hand and Reconstructive Microsurgery, National University Hospital, Singapore.
Mod Pathol
December 2024
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence (CPACE), University of Pittsburgh, School of Medicine, Pittsburgh, Pennsylvania. Electronic address:
The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques.
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