The neurological severity of a spinal cord injury (SCI) is commonly classified according to the American Spinal Injury Association (ASIA) Impairment Scale (AIS). The aim of this study was to assess the course of the AIS following a SCI, and to discern the nature of any changes in the classification that occur. Assessments were performed in a European cohort of SCI patients within two weeks and at one, three, six and twelve months after the initial injury. Overall, about 70% of the patients initially diagnosed as AIS A did not convert, as did 90% of the AIS D patients. When only evaluating patients with complete datasets, 68% did not convert, while the AIS category improved in 30% of patients and deteriorated in 2% of patients. A change in the last sacral segments (40%), motor improvement (31%), sensory improvement (19%) and a change in the neurological level of the SCI (10%) contributed to or accompanied the AIS conversion. When the AIS remained unchanged between successive assessment points, there was no change in the number of muscles graded three or more (NMG3+) in 73% of the transitions. An improvement in AIS was associated with a gain in NMG3+ in 49% of the transitions, while an aggravation in AIS was accompanied by a loss in NMG3+ in 10% of the transitions. These results, documenting a substantial amount of spontaneous AIS conversions, should be taken into consideration when designing clinical trials to assess the effects of potential new treatments for SCI. This article has been peer-reviewed and accepted for publication, but has yet to undergo copyediting and proof correction. The final published version may differ from this proof. 2 AbstractThe neurological severity of a spinal cord injury (SCI) is commonly classified according to the American Spinal Injury Association (ASIA) Impairment Scale (AIS).The aim of this study was to assess the course of the AIS following a SCI, and to discern the nature of any changes in the classification that occur. Assessments were performed in a European cohort of SCI patients within two weeks and at one, three,

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This paper suggests a new way of comparing and analyzing causal theories. The main contribution is a meta-model that represents causal theories and a taxonomy of inter-theory relationships. The intertheory relationships can be automatically calculated for two theories that are described with the metamodel. Two visualizations are presented with which to analyze set of theories: the inter-theory relationship matrix and the theory evolution graph. An exemplary application of the approach is shown for a small set of information systems theories. The proposed approach should help researchers improve their understanding of the contribution and evolution of theories.