{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:32:02Z","timestamp":1789399922240,"version":"build-2803163510"},"reference-count":37,"publisher":"National Athletic Trainers' Association","issue":"11-12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,11,1]]},"abstract":"<jats:sec><jats:title>Context<\/jats:title><jats:p>Multiple clinical evaluation tools exist for adolescent concussion with various degrees of correlation, presenting challenges for clinicians in identifying which elements of these tools provide the greatest diagnostic utility.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>To determine the combination of elements from 4 commonly used clinical concussion batteries that maximize discrimination of adolescents with concussion from those without concussion.<\/jats:p><\/jats:sec><jats:sec><jats:title>Design<\/jats:title><jats:p>Cross-sectional study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>Suburban school and concussion program of a tertiary care academic center.<\/jats:p><\/jats:sec><jats:sec><jats:title>Patients or Other Participants<\/jats:title><jats:p>A total of 231 participants with concussion (from a suburban school and a concussion program) and 166 participants without concussion (from a suburban school) between the ages of 13 and 19 years.<\/jats:p><\/jats:sec><jats:sec><jats:title>Main Outcome Measure(s)<\/jats:title><jats:p>Individual elements of the visio-vestibular examination (VVE), Sport Concussion Assessment Tool, fifth edition (SCAT5; including the modified Balance Error Scoring System), King-Devick test (K-D), and Postconcussion Symptom Inventory (PCSI) were evaluated. The 24 subcomponents of these tests were grouped into interpretable factors using sparse principal component analysis. The 13 resultant factors were combined with demographic and clinical covariates into a logistic regression model and ranked by frequency of inclusion into the ideal model, and the predictive performance of the ideal model was compared with each of the clinical batteries using the area under the receiver operating characteristic curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A cluster of 4 factors (factor 1 [VVE saccades and vestibulo-ocular reflex], factor 2 [modified Balance Error Scoring System double-legged stance], factor 3 [SCAT5\/PCSI symptom scores], and factor 4 [K-D completion time]) emerged. A model fit with the top factors performed as well as each battery in predicting concussion status (AUC = 0.816 [95% CI = 0.731, 0.889]) compared with the SCAT5 (AUC = 0.784 [95% CI = 0.692, 0.866]), PCSI (AUC = 0.776 [95% CI = 0.674, 0.863]), VVE (AUC = 0.711 [95% CI = 0.602, 0.814]), and K-D (AUC = 0.708 [95% CI = 0.590, 0.819]).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>A multifaceted assessment for adolescents with concussion, comprising symptoms, attention, balance, and the visio-vestibular system, is critical. Current diagnostic batteries likely measure overlapping domains, and the sparse principal component analysis demonstrated strategies for streamlining comprehensive concussion assessment across a variety of settings.<\/jats:p><\/jats:sec>","DOI":"10.4085\/1062-6050-0020.22","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T19:21:49Z","timestamp":1673896909000},"page":"962-973","source":"Crossref","is-referenced-by-count":5,"title":["Maximizing the Accuracy of Adolescent Concussion Diagnosis Using Individual Elements of Common Standardized Clinical Assessment Tools"],"prefix":"10.4085","volume":"58","author":[{"given":"Daniel J.","family":"Corwin","sequence":"first","affiliation":[{"name":"*Center for Injury Research and Prevention, Children\u2019s Hospital of Philadelphia, PA"},{"name":"\u2020Division of Emergency Medicine, Children\u2019s Hospital of Philadelphia, 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