Abstract
Since Barth and colleagues' seminal study used baseline neuropsychological testing as a model for sports concussion management, many collegiate sports medicine programs have adopted variations of their approach. In our chapter in the first edition of this book, and further refined in a subsequent article, we laid out an evidence-based strategy for the use of neuropsychological tests in concussion management when no baseline testing has been conducted. In this chapter, we provide a test of this evidence-based model. The model involves an algorithm using base rates of impairment in a typical neurocognitive sports concussion battery involving 17 indices, with decision rules that differ slightly for males and females. Applying the algorithm, we identified 120 "Recovered" and 20 "Not Recovered" concussed collegiate athletes. Outcome variables were cognitive indices not included in the original algorithm: (1) Immediate and Delayed Recall on the Affective Word List (AWL); (2) Immediate and Delayed Recall on the Story subtest from the Rivermead Behavioral Memory Test (RBMT); and (3) Total Words Generated on the Controlled Oral Word Association Test (COWAT). We found that the Not Recovered group performed significantly worse than the Recovered group on the outcome variables overall, with significant univariate effects for the RBMT Immediate and Delayed Recall and the AWL Immediate Recall. Our proposed neuropsychological concussion management algorithm provides a testable evidence-based model, for which we found some support with the data presented. Further tests of this model using other populations and additional outcome variables are needed to verify its validity.
Original language | English (US) |
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Title of host publication | Concussions in Athletics |
Subtitle of host publication | From Brain to Behavior |
Publisher | Springer International Publishing |
Pages | 19-35 |
Number of pages | 17 |
ISBN (Electronic) | 9783030755645 |
ISBN (Print) | 9783030755638 |
DOIs | |
State | Published - Aug 18 2021 |
All Science Journal Classification (ASJC) codes
- General Medicine