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Objectives: The aims of this study were to evaluate the relationship between pre-match rest days, match location, time of season, and match outcome with diagnosed concussion and use of the Concussion Interchange Rule (CIR) during the 2014 NRL season. Design: Descriptive observational study. Methods: This study reviewed all instances of diagnosed concussions, CIR activations, and match characteristics during the 2014 NRL season. Information pertaining to the use of the CIR was obtained from the league. Each club provided information of diagnosed concussion. Results: A total of 402 NRL games were analyzed, the concussion interchange rule was activated 167 times, and 60 concussions were diagnosed in 53 of the matches. A significantly greater proportion of concussions occurred during the first match of the season (38% vs. 15% of in-season matches; p = 0.02). Days of rest, match location, season stage, and match outcome were not associated with a higher proportion of concussions. Furthermore, we did not detect a significant association between CIR activation and days of rest prior to the match, opponent rest prior to the match, match location, round of the season, or match outcome. Conclusions: A significantly greater proportion of concussions were diagnosed during the first match of the season. Days between matches, opponent days of rest, match location, or other rounds (aside from round 1) of the season were not associated with CIR activation or diagnosed concussion. Varied game scheduling during the season does not appear to be associated with increased risk for concussion or use of the CIR in the NRL. (C) 2019 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Currently, discourse relation recognition (DRR), which is not directly marked with connectives, is a challenging task. Traditional approaches for implicit DRR in Chinese have focused on exploring the concepts and features of words; however, these approaches have only yielded slow progress. Moreover, the lack of Chinese labeled data makes it more difficult to complete this task with high accuracy. To address this issue, we propose a novel hybrid DRR model combining a pretrained language model, namely bidirectional encoder representations from transformers (BERT), with recurrent neural networks. We use BERT as a text representation and pretraining model. In addition, we apply a tree structure to the implicit DRR in Chinese to produce hierarchical classes. The 19-class F1 score of our proposed method can reach 74.47% on the HIT-CIR Chinese discourse relation corpus. The attained results showed that the use of BERT and the proposed tree structure forms a novel and precise method that can automatically recognize the implicit relations of Chinese discourse.