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Context: The Athletic Training Locations and Services (ATLAS) Annual Report suggested that athletic trainer (AT) employment status differed based on geographic locale. However, the influence of geographic locale and school size on AT employment is unknown. Objective: To determine if differences existed in the odds of having AT services by locale for public and private schools and by student enrollment for public schools. Design: Cross-sectional study. Setting: Public and private secondary schools with athletics programs. Patients or Other Participants: Data from 20 078 US public and private secondary schools were obtained. Main Outcome Measures(s): Data were collected by the ATLAS Project. Athletic trainer employment status, locale (city, suburban, town, or rural) for public and private schools, and school size category (large, moderate, medium, or small) only for public schools were obtained. The employment status of ATs was examined for each category using odds ratios. Logistic regression analysis produced a prediction model. Results: Of the 19 918 public and private schools with available AT employment status and locale, suburban schools had the highest access to AT services (80.1%) with increased odds compared with rural schools (odds ratio = 3.55 [95% CI = 3.28, 3.85]). Of 15 850 public schools with known AT employment status and student enrollment, large schools had the highest rate of AT services (92.1%) with nearly 18.5 times greater odds (odds ratio = 18.49 [95% CI = 16.20, 21.08]) versus small schools. The logistic model demonstrated that the odds of access to an AT increased by 2.883 times as the school size went up by 1 category. Conclusions: Nationally, suburban schools and large public schools had the greatest access to AT services compared with schools that were in more remote areas and with lower student enrollment. These findings elucidate the geographic locales and student enrollment levels with the highest prevalence of AT services. Ó by the National Athletic Trainers’ Association, Inc
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Current methods of concussion assessment lack the objectivity and reliability to detect neurological injury. This multi-site study uses combinations of neuroimaging (diffusion tensor imaging and resting state functional MRI) and cognitive measures to train algorithms to detect the presence of concussion in university athletes. Athletes (29 concussed, 48 controls) completed symptom reports, brief cognitive evaluation, and MRI within 72 h of injury. Hierarchical linear regression compared groups on cognitive and neuroimaging measures while controlling for sex and data collection site. Logistic regression and support vector machine models were trained using cognitive and neuroimaging measures and evaluated for overall accuracy, sensitivity, and specificity. Concussed athletes reported greater symptoms than controls (∆R2 = 0.32, p < .001), and performed worse on tests of concentration (∆R2 = 0.07, p < .05) and delayed memory (∆R2 = 0.17, p < .001). Concussed athletes showed lower functional connectivity within the frontoparietal and primary visual networks (p < .05), but did not differ on mean diffusivity and fractional anisotropy. Of the cognitive measures, classifiers trained using delayed memory yielded the best performance with overall accuracy of 71%, though sensitivity was poor at 46%. Of the neuroimaging measures, classifiers trained using mean diffusivity yielded similar accuracy. Combining cognitive measures with mean diffusivity increased overall accuracy to 74% and sensitivity to 64%, comparable to the sensitivity of symptom report. Trained algorithms incorporating both MRI and cognitive performance variables can reliably detect common neurobiological sequelae of acute concussion. The integration of multi-modal data can serve as an objective, reliable tool in the assessment and diagnosis of concussion.
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- Journal Article (2)
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- English (2)