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Do Mild Traumatic Brain Injury Severity Sub-classification Systems Help to Identify People Who Go on to Experience Long-Term Symptoms?

  • Alice Theadom (a1), Suzanne Barker-Collo (a2), Andrea Greenwood (a2), Priya Parmar (a1), Kelly Jones (a1), Nicola Starkey (a3), Kathryn McPherson (a4) and Valery L. Feigin (a1)...

Objective: To identify the systems available to sub-classify mild traumatic brain injury (TBI) and to determine their utility in predicting 1-year outcome.

Methods: A systematic review to identify mild-TBI sub-classification systems was conducted until March 2016. The identified systems were applied to a cohort of N = 290 adults who had experienced a mild-TBI, and who had been assessed for post-concussion symptoms 1-year post injury. ANOVAs and regression models were used to determine whether each sub-classification system could distinguish between outcomes and to explore their contribution to explaining variance in post-concussion symptoms 1-year post injury.

Results: Nineteen sub-classification systems for mild-TBI met the inclusion criteria for this review. The Saal (1991) classification system significantly differentiated the experience of post-concussion symptoms in our cohort 1-year post injury (F = 2.39, p = 0.05). However, the findings did not remain significant following correction for multiple comparisons and inclusion of socio-demographic and contextual factors in the regression model.

Conclusions: Current sub-classification systems fail to explain much of the variance in post-concussion symptoms 1 year following mild-TBI. Further research is needed to identify the factors (including socio-demographic and contextual factors) to determine, who may be at risk of developing persistent post-concussion symptoms.

Corresponding author
Address for correspondence: Alice Theadom, PhD, National Institute for Stroke and Applied Neurosciences, Faculty of Health and Environmental Studies, Auckland University of Technology, AUT North Shore Campus, AR417, 90 Akoranga Dr, Northcote 0627, Private Bag 92006, Auckland 1142, New Zealand. E-mail:
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