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      Tensiomyography Allows to Discriminate between Injured and Non-Injured Biceps Femoris Muscle

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      Biology
      MDPI AG

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          Abstract

          The hamstring muscle group is the most frequently injured muscle group in non-contact muscle injuries in sports involving high-speed running. A total of 84% of hamstring injuries affect the biceps femoris (BF) muscle. Clinical assessments and magnetic resonance imaging (MRI) are routinely used for diagnosis and plan management. MRI-negative scans for clinically diagnosed hamstring injuries range from 14% to 45%. We tested the hypothesis that the functional differences between injured and non-injured BF assessed by tensiomyography can be used for diagnostic and classification purposes. We compared an injured group of 53 international-level soccer players and sprinters with 53 non-injured international-level soccer players and sprinters of both sexes. Comparing the injured vs. non-injured athletes and the left vs. right side in all of the athletes, we used the percentage of absolute differences in the BF contraction time (Tc) to classify non-injured and injured BF muscles. The receiver operating characteristic (ROC) curve and the area under the curve (AUC) and the precision–recall curve (PRC) were used to measure the classification accuracy and to identify cut-off limits using the Tc differences. There was a very high ROC AUC value of 0.981 (SE = 0.009, p < 0.000), with 98.11% of the injured muscles being correctly classified (cut-off point 12.50% on Tc differences), and an AUPRC value of 0.981, with association classification criteria at >9.87. Tensiomyography has a high predictive ability to discriminate between injured and non-injured BF non-invasively and functionally.

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          Most cited references55

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          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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            A method of comparing the areas under receiver operating characteristic curves derived from the same cases.

            Receiver operating characteristic (ROC) curves are used to describe and compare the performance of diagnostic technology and diagnostic algorithms. This paper refines the statistical comparison of the areas under two ROC curves derived from the same set of patients by taking into account the correlation between the areas that is induced by the paired nature of the data. The correspondence between the area under an ROC curve and the Wilcoxon statistic is used and underlying Gaussian distributions (binormal) are assumed to provide a table that converts the observed correlations in paired ratings of images into a correlation between the two ROC areas. This between-area correlation can be used to reduce the standard error (uncertainty) about the observed difference in areas. This correction for pairing, analogous to that used in the paired t-test, can produce a considerable increase in the statistical sensitivity (power) of the comparison. For studies involving multiple readers, this method provides a measure of a component of the sampling variation that is otherwise difficult to obtain.
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              Hamstring strain injuries: factors that lead to injury and re-injury.

              Hamstring strain injuries (HSIs) are common in a number of sports and incidence rates have not declined in recent times. Additionally, the high rate of recurrent injuries suggests that our current understanding of HSI and re-injury risk is incomplete. Whilst the multifactoral nature of HSIs is agreed upon by many, often individual risk factors and/or causes of injury are examined in isolation. This review aims to bring together the causes, risk factors and interventions associated with HSIs to better understand why HSIs are so prevalent. Running is often identified as the primary activity type for HSIs and given the high eccentric forces and moderate muscle strain placed on the hamstrings during running these factors are considered to be part of the aetiology of HSIs. However, the exact causes of HSIs remain unknown and whilst eccentric contraction and muscle strain purportedly play a role, accumulated muscle damage and/or a single injurious event may also contribute. Potentially, all of these factors interact to varying degrees depending on the injurious activity type (i.e. running, kicking). Furthermore, anatomical factors, such as the biarticular organization, the dual innervations of biceps femoris (BF), fibre type distribution, muscle architecture and the degree of anterior pelvic tilt, have all been implicated. Each of these variables impact upon HSI risk via a number of different mechanisms that include increasing hamstring muscle strain and altering the susceptibility of the hamstrings to muscle damage. Reported risk factors for HSIs include age, previous injury, ethnicity, strength imbalances, flexibility and fatigue. Of these, little is known, definitively, about why previous injury increases the risk of future HSIs. Nevertheless, interventions put in place to reduce the incidence of HSIs by addressing modifiable risk factors have focused primarily on increasing eccentric strength, correcting strength imbalances and improving flexibility. The response to these intervention programmes has been mixed with varied levels of success reported. A conceptual framework is presented suggesting that neuromuscular inhibition following HSIs may impede the rehabilitation process and subsequently lead to maladaptation of hamstring muscle structure and function, including preferentially eccentric weakness, atrophy of the previously injured muscles and alterations in the angle of peak knee flexor torque. This remains an area for future research and practitioners need to remain aware of the multifactoral nature of HSIs if injury rates are to decline.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                BBSIBX
                Biology
                Biology
                MDPI AG
                2079-7737
                May 2022
                May 13 2022
                : 11
                : 5
                : 746
                Article
                10.3390/biology11050746
                2b22a2fc-cb1f-4a22-9235-d1adb78116ef
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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