A Novel Fragment Specific Classification of Complex Olecranon Fractures: 3-Dimensional Model Design, Radiological Validation, and Proposed Surgical Algorithm.
Journal of Shoulder and Elbow Surgery（2024）
Hop Sacre Coeur Montreal
Abstract
Background: Current classifications for proximal ulna fracture patterns rely on qualitative data and cannot inform surgical planning. We propose a new classification system based on a biological and anatomical stress analysis. Our hypothesis is that fragment types in complex fractures can be predicted by the tendon and ligament attachments on the proximal ulna. Methods: First, we completed a literature review to identify quantitative data on proximal ulna soft tissue attachments. On this basis, we created a 3 -dimensional model of ulnar anatomy with SliceOMatic and Catia V5R20 software and determined likely locations for fragments and fracture lines. The second part of the study was a retrospective radiological study. A level -1 trauma radiological database was used to identify computed tomography scans of multifragmentary olecranon fractures from 2009 to 2021. These were reviewed and classified according to the '' fragment specific "classification and compared to the Mayo and the Schatzker classifications. Results: Twelve articles (134 elbows) met the inclusion criteria and 7 potential fracture fragments were identified. The radiological study included 67 preoperative computed tomography scans (mean 55 years). The fragments identified were the following: posterior (40%), intermediate (42%), tricipital ( 100%), supinator crest (25%), coronoid (18%), sublime tubercle (12%), and anteromedial facet (18%). Eighteen cases (27%) were classified as Schatzker D (comminutive) and 21 (31%) Mayo 2B (stable comminutive). Inter -rater correlation coefficient was 0.71 among 3 observers. Conclusion: This proposed classification system is anatomically based and considers the deforming forces from ligaments and tendons. Having a more comprehensive understanding of complex proximal ulna fractures would lead to more accurate fracture evaluation and surgical planning. Level of evidence: Basic Science Study; Development of Classification System (c) 2024 Journal of Shoulder and Elbow Surgery Board of Trustees. All rights reserved.
MoreTranslated text
Key words
Classification,olecranon,ulna,fracture,fragment specific
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined