- Title
- An updated subsequent injury categorisation model (SIC-2.0) : Data-driven categorisation of subsequent injuries in sport
- Creator
- Toohey, Liam; Drew, Michael; Fortington, Lauren; Finch, Caroline; Cook, Jill
- Date
- 2018
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/165462
- Identifier
- vital:13336
- Identifier
-
https://doi.org/10.1007/s40279-018-0879-3
- Identifier
- ISBN:0112-1642
- Abstract
- Background: Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging. Objectives: The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset. Methods: The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed. Results: An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1–12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%. Conclusions: The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport.
- Publisher
- Springer International Publishing
- Relation
- Sports Medicine Vol. 48, no. 9 (2018), p. 2199-2210
- Rights
- Copyright © 2018, Springer International Publishing AG, part of Springer Nature.
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0913 Mechanical Engineering; 1106 Human Movement and Sports Science; 1302 Curriculum and Pedagogy; Injury categorisation model; Subsequent injury; Sport injury
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