WeChat Mini Program
Old Version Features

Analysis of Player Performance and Financial Costs Associated with Implementation of an Updated National Hockey League Concussion Protocol: A Retrospective Comparative Study

ORTHOPAEDIC JOURNAL OF SPORTS MEDICINE(2024)

Henry Ford Hosp

Cited 1|Views3
Abstract
Background: An updated National Hockey League (NHL) concussion protocol (NHLCP) was established in the 2016-2017 season to mitigate the negative outcomes of sport-related concussions. However, few studies on the effects of implementing the NHLCP have been performed. Purpose: To define concussion incidence and investigate differences in NHL player performance after a concussion during periods before and after NHLCP implementation and assess the financial impact on NHL teams associated with NHLCP implementation. Study Design: Cohort study; Level of evidence, 3 Methods: This was a retrospective review of NHL players who sustained a concussion before (2000-2001 to 2015-2016 seasons) and after (2016-2017 to 2020-2021 seasons) implementing the NHLCP (pre-NHLCP and post-NHLCP groups). For each group, multiple performance metrics—including 30 days, 1 season, and 3 seasons before and after concussion—were compared for both groups. Return to play, total concussion cost, and association of return to play with cost were investigated using regression analysis. Results: A total of 452 players (423 skaters, 29 goalies) sustained concussions during the study period, including 331 players (315 skaters, 16 goalies) in the pre-NHLCP group and 121 players (108 skaters, 13 goalies) in the post-NHLCP group. For both groups, no significant differences in standard performance were observed during the 30-day and 1-season periods before and after concussion. The mean return to play was significantly higher in the pre-NHLCP group than in the post-NHLCP group (20.1 vs 15.7 days; P = .022). The mean adjusted player salary was not different between groups; nonetheless, the mean adjusted replacement player salary was significantly higher in the post-NHLCP group ($744,505 vs $896,942; P = .032). The mean cost of time missed did not differ between groups. The mean return to play time significantly decreased over the entire study period ( R2 = 0.33; P = .005), and the mean return to play time was positively associated with cost R2 = 0.215; P = .030). Conclusion: Concussion incidence did not change after implementation of the updated NHLCP; nonetheless, players had significantly less missed time from injury after protocol implementation. Changes in player performance 30 days and 1 year before and after concussion injury were not different before and after NHLCP implementation. No differences were found in the financial cost of concussions between the pre- and post-NHLCP groups, and missed time was significantly correlated with mean cost from missed time.
More
Translated text
Key words
concussion,concussion protocol,National Hockey League,sport performance
上传PDF
Bibtex
收藏
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined