scholarly journals Experimental Comparison of Autograft and DBM Flex (Grafton) for Spinal Lumbar Fusion in Rabbits

2021 ◽  
Vol 6 (3) ◽  
pp. 153-157
Author(s):  
Cem DEMİREL ◽  
Dursun TÜRKÖZ ◽  
Tuncay YİLMAZ
2011 ◽  
Vol 16 (2) ◽  
pp. 8-9
Author(s):  
Marjorie Eskay-Auerbach

Abstract The incidence of cervical and lumbar fusion surgery has increased in the past twenty years, and during follow-up some of these patients develop changes at the adjacent segment. Recognizing that adjacent segment degeneration and disease may occur in the future does not alter the rating for a cervical or lumbar fusion at the time the patient's condition is determined to be at maximum medical improvement (MMI). The term adjacent segment degeneration refers to the presence of radiographic findings of degenerative disc disease, including disc space narrowing, instability, and so on at the motion segment above or below a cervical or lumbar fusion. Adjacent segment disease refers to the development of new clinical symptoms that correspond to these changes on imaging. The biomechanics of adjacent segment degeneration have been studied, and, although the exact mechanism is uncertain, genetics may play a role. Findings associated with adjacent segment degeneration include degeneration of the facet joints with hypertrophy and thickening of the ligamentum flavum, disc space collapse, and translation—but the clinical significance of these radiographic degenerative changes remains unclear, particularly in light of the known presence of abnormal findings in asymptomatic patients. Evaluators should not rate an individual in anticipation of the development of changes at the level above a fusion, although such a development is a recognized possibility.


Author(s):  
David J. Hardisty ◽  
Katherine J. Thompson ◽  
David H. Krantz ◽  
Elke U. Weber

2020 ◽  
Vol 39 (4) ◽  
pp. 5905-5914
Author(s):  
Chen Gong

Most of the research on stressors is in the medical field, and there are few analysis of athletes’ stressors, so it can not provide reference for the analysis of athletes’ stressors. Based on this, this study combines machine learning algorithms to analyze the pressure source of athletes’ stadium. In terms of data collection, it is mainly obtained through questionnaire survey and interview form, and it is used as experimental data after passing the test. In order to improve the performance of the algorithm, this paper combines the known K-Means algorithm with the layering algorithm to form a new improved layered K-Means algorithm. At the same time, this paper analyzes the performance of the improved hierarchical K-Means algorithm through experimental comparison and compares the clustering results. In addition, the analysis system corresponding to the algorithm is constructed based on the actual situation, the algorithm is applied to practice, and the user preference model is constructed. Finally, this article helps athletes find stressors and find ways to reduce stressors through personalized recommendations. The research shows that the algorithm of this study is reliable and has certain practical effects and can provide theoretical reference for subsequent related research.


Author(s):  
S. Ouenzerfi ◽  
T. Barreteau ◽  
C. Petit ◽  
Valerie Sartre ◽  
Jocelyn Bonjour ◽  
...  

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