Introduction
Increased risk of musculoskeletal (MSK) injury post-concussion has been reported in collegiate athletes, yet it is unknown if professional football athletes are at the same risk of secondary injury. The objective of this study was to determine if the risk of MSK injury in National Football League (NFL) athletes increases after concussion.
Methods
NFL injury reports from 2013 to 2017 were collected from public websites. Concussed athletes (n=91) were equally matched to a non-injured control and an athlete with an incident of musculoskeletal (MSK) injury.
Results
Following their return to sport, concussed athletes were 2.35 times more likely to have a subsequent MSK injury relative to non-injured controls (95% CI: 2.35 [1.25, 4.44],
P
= 0.01), but were no more likely than athletes with an incident MSK injury (
P
= 0.55). Likewise, athletes with an incident MSK injury were no more likely to have a subsequent MSK injury than controls (
P
= 0.08).
Discussion
Increased odds of MSK injury in the 12-week period following a concussion in professional football athletes warrants future research on the acute effects of concussion and the relationship to MSK injury risk.
Missing data presents a challenge for machine learning applications specifically when utilizing electronic health records to develop clinical decision support systems. The lack of these values is due in part to the complex nature of clinical data in which the content is personalized to each patient. Several methods have been developed to handle this issue, such as imputation or complete case analysis, but their limitations restrict the solidity of findings. However, recent studies have explored how using some features as fully available privileged information can increase model performance including in SVM. Building on this insight, we propose a computationally efficient kernel SVM-based framework (l2-SVMp+) that leverages partially available privileged information to guide model construction. Our experiments validated the superiority of l2-SVMp+ over common approaches for handling missingness and previous implementations of SVMp+ in both digit recognition, disease classification and patient readmission prediction tasks. The performance improves as the percentage of available privileged information increases. Our results showcase the capability of l2-SVMp+ to handle incomplete but important features in real-world medical applications, surpassing traditional SVMs that lack privileged information. Additionally, l2-SVMp+ achieves comparable or superior model performance compared to imputed privileged features.
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