Clinical Utility of GRASSP in Traumatic Tetraplegia: A Narrative Review and Retrospective Analysis Incorporating Machine Learning with an Explainability Framework

Study Design: Narrative Review and Retrospective Post-Hoc Analysis Objectives: 1. Summarize GRASSP literature and provide evidentiary tables to support clinical research decision making 2. Establish enrollment cut-offs for acute and cross-sectional chronic samples 3. Define recovery profiles of GRASSP subtests and typical relationships of impairment to function. Methods: A literature review of existing GRASSP publications was conducted. Two datasets were created from three historical cohorts of data collected for GRASSP development. A longitudinal dataset includes a total of 182 individuals with tetraplegia, each of whom was assessed at 0, 1, 3, and 6 months after injury (728 observations). The cross-sectional chronic dataset includes a total of 254 individuals with tetraplegia. The sample was stratified by AIS at Exam Stage 1. AIS classification of A-B were assigned to the motor complete group and AIS classification of C-D were assigned to the motor incomplete group Analysis: Literature was reviewed and knowledge synthesized. Data was summarized with descriptive statistics. Machine learning was applied to both datasets for prediction. Results: Evidentiary tables summarize three versions of GRASSP and synthesize the literature to define the clinical research utility. Three figures define the discriminative groupings of GRASSP subtest scores along with recovery profiles. Machine learning identifies the predictive qualities of GRASSP Strength. Conclusion: GRASSP use recommendations are meaningful to researchers/clinicians for implementation and sample characteristics of historical data provide insight for researchers into expected outcomes of individuals with traumatic tetraplegia.