Translation into Spanish and proposal to modify the Orchard Sports Injury Classification System (OSICS) version 12

Jiménez, Juan de Dios Beas; Garrigosa, Antonio León; Cuevas, Pilar Doñoro; Riaza, Luis; Peirau, Xavier; Alonso, Juan Manuel; Recio, María Isabel Álvarez; Boladós, Cristian Cofré

doi:10.1016/j.apunsm.2020.05.002

Cited by 5 publications

(2 citation statements)

References 16 publications

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“…Orchard et al explained the process of identifying new coding categories and updated new versions of the Sports Medicine Diagnostic Coding System and Sports Injury Disease Classification System to implement the new consensus categories [ 2 ]. Bj et al published a Spanish translation of the 12th edition of the Sports Injury Classification System, known as the Orchard Sports Injury Classification System, and proposed a numerical code for the impact of injuries on motor function [ 3 ]. Knee injuries are closely related to the development of knee osteoarthritis.…”

Section: Related Workmentioning

confidence: 99%

Sports Injury Identification Method Based on Machine Learning Model

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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With the increasingly fierce competition in international competitive sports, the momentum of special training has increased. Sports injuries are becoming more and more serious, which restricts the further improvement of the level of athletes. How to solve the problem of prevention, treatment, and rehabilitation of sports injuries, so as to ensure the normal training and competition of athletes, is an important part of sports work. Machine learning can solve large-scale data problems that cannot be solved by human beings at present and has strong self-learning ability, self-optimization ability, and strong generalization ability. Therefore, the purpose of this study is to understand the characteristics of rhythmic gymnastics injuries and analyze their causes by investigating the injury status of elite rhythmic gymnasts. According to the characteristics of the project, the injury characteristics of the athletes themselves, and other factors, using scientific qualitative and quantitative indicators, the injury risk of key athletes in rhythmic gymnastics was evaluated. It also provides theoretical and practical references for preventing sports injuries, formulating and implementing sports injury rehabilitation programs. The experimental results show that the female vaulting risk in the five risk categories fluctuates from 179.62 to 365.8, ranking the first in the risk of acute sports injury.

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Section: Related Workmentioning

confidence: 99%

Sports Injury Identification Method Based on Machine Learning Model

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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“…Artificial neural network has good robustness, selforganization, adaptability, parallel processing ability, distributed storage, and other characteristics, as well as excellent fault tolerance and nonlinear approximation ability. At present, artificial neural network algorithm is widely used in complex system hardware fault detection, medical diagnosis, medical image processing and other complex tasks, classification, and prediction and achieved good results [6][7][8][9]. In terms of risk prevention of sports injury, dfmurphy and rbohr, on the basis of previous studies, divided the risk factors of sports injury into two categories: internal factors and external factors.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

He¹

2021

Complexity

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Sports injury is a common problem in athletes’ training. The sports injury assessment model is a physical method to determine the sports injury attributes of specific parts by predicting and evaluating the risk of sports injury. In this paper, we use a neural network to realize big data analysis of sports injury data. Big data network is a method of capturing Internet information by means of cloud computing, which is usually used in the construction of Wan and LAN. This paper analyzes the source of sports risk and the main injury factors, designs the sports injury estimation model based on big data analysis, establishes a new assessment model based on RBF neural network, and builds the big data network environment required for the model operation by improving the topological structure, combining big data and deep neural network. In the built environment, the risk assessment of sports injury can be completed by determining the risk source and identifying the risk factors. The realization of the model can be constrained by the uncertainty conditions so that it can achieve a good operation state.

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Return to Play Prediction Accuracy of the MLG-R Classification System for Hamstring Injuries in Football Players: A Machine Learning Approach

et al. 2022

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BackgroundMuscle injuries are one of the main daily problems in sports medicine, football in particular. However, we do not have reliable means to predict the outcome, i.e. return to play (RTP) from severe injury. The aim of the present study was to evaluate the capability of the MLG-R classification system to grade hamstring muscle injuries by severity, offer a prognosis for the RTP, and identify injuries with higher risk of reinjury. And to assess the consistency of our proposed system by investigating its intra-and inter-observer reliability. MethodsAll male professional football players from FC Barcelona (FCB)-senior A and B and the two U-19 teams-with injuries occurred between February 2010 and February 2020 were reviewed. Only players with clinical presentation of hamstring muscle injury, with complete clinic information and magnetic resonance images (MRI), were included. Three different statistical and machine learning approaches (linear regression, random forest, and XGBoost) were used to assess the importance of each factor of the MLG-R classification system in determining the RTP as well as to offer a prediction of the expected RTP. We used the Cohen's kappa and the intraclass correlation coefficient (ICC) to assess the intra-and interobserver reliability. ResultsBetween 2010 and 2020, 76 hamstring injuries corresponding to 42 different players were identified, of which 50 (65.8%) were Grade 3 r , 54 (71.1%) affected the biceps femoris long head (BFlh), and 33 of the 76 (43.4%) were located at the proximal myotendinous junction (MTJ). The mean RTP for Grade 2, 3, and 3 r injuries were 14.3, 12.4, and 37 days, respectively. Injuries affecting the proximal MTJ had a mean RTP of 31.7 days while those affecting the distal part of the MTJ had a mean RTP of 23.9 days. The analysis of the grade 3r BFlh injuries located at the FT showed a median RTP time of 56 days while the injuries located at the central tendon had a shorter RTP of 24 days (p=0.038). The statistical analysis showed an excellent predictive power of the MLG-R classification system with a mean absolute error of 9.8 days and an R-squared of 0.48. The most important factors to determine the RTP were if the injury was at the free tendon (FT) of the BFlh or if it was a Grade 3 r injury. For all the items of the MLG-R classification the intra-and inter-observer reliability was excellent (k > 0.93) except for fibers blurring (κ = 0.68). ConclusionThe main determinant for long RTP after hamstring injury is the injury affecting the connective tissue structures of the hamstring.We developed a reliable hamstring muscle injury classification system based on MRI that showed excellent results in terms of reliability, prognosis capability and objectivity. It is easy to use in clinical 2 daily practice, and can be further adapted to future knowledge. The adoption of this system by the medical community would allow to uniform diagnosis leading to better injury management.

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Translation into Spanish and proposal to modify the Orchard Sports Injury Classification System (OSICS) version 12

Cited by 5 publications

References 16 publications

Sports Injury Identification Method Based on Machine Learning Model

Sports Injury Identification Method Based on Machine Learning Model

[Retracted] Early Warning Model of Sports Injury Based on RBF Neural Network Algorithm

Return to Play Prediction Accuracy of the MLG-R Classification System for Hamstring Injuries in Football Players: A Machine Learning Approach

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