Background: The tackle is the game event in rugby union most associated with injury. This study's main aims were to measure tackle characteristics from video using a qualitative protocol, to assess whether the characteristics differed by level of play, and to measure the associations between tackle characteristics and injury.
Methods: A cohort study was undertaken. The cohort comprised male rugby players in the following levels: younger than 15 yr, 18 yr, and 20 yr, grade, and elite (Super 12 and Wallabies). All tackle events and technique characteristics were coded in 77 game halves using a standardized qualitative protocol. Game injuries and missed-game injuries were identified and correlated with tackle events.
Results: A total of 6618 tackle events, including 81 resulting in a game injury, were observed and coded in the 77 game halves fully analyzed (145 tackle events per hour). An increase in the proportion of active shoulder tackles was observed from younger than 15 yr (13%) to elite (31%). Younger players engaged in more passive tackles and tended to stay on their feet more than experienced players. Younger than 15 yr rugby players had a significantly lower risk of tackle game injury compared with elite players. No specific tackle technique was observed to be associated with a significantly increased risk of game injury. There was a greater risk of game injury associated with two or more tacklers involved in the tackle event, and the greatest risk was associated with simultaneous contact by tacklers, after adjusting for level of play.
Conclusions: Tackle characteristics differed between levels of play. The number of tacklers and the sequence of tackler contact with the ball carrier require consideration from an injury prevention perspective.
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http://dx.doi.org/10.1249/MSS.0b013e3181c07b5b | DOI Listing |
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January 2025
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Deep learning methods have significantly improved medical image analysis, particularly in detecting COVID-19 chest X-rays. Nonetheless, these methodologies frequently inhibit some drawbacks, such as limited interpretability, extensive computational resources, and the need for extensive datasets. To tackle these issues, we introduced two novel algorithms: the Dynamic Co-Occurrence Grey Level Matrix (DC-GLM) and the Contextual Adaptation Multiscale Gabor Network (CAMSGNeT).
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