An updated subsequent injury categorisation model (SIC-2.0) : data-driven categorisation of subsequent injuries in sport
Deutscher übersetzter Titel: | Ein aktualisiertes Kategorisierungsmodell für Folgeverletzungen (SIC-2.0) : datengestützte Kategorisierung von Folgeverletzungen im Sport |
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Autor: | Toohey, Liam A.; Drew, Michael K.; Fortington, Lauren V.; Finch, Caroline F.; Cook, Jill L. |
Erschienen in: | Sports medicine |
Veröffentlicht: | 48 (2018), 9, S. 2199-2210, Lit. |
Format: | Literatur (SPOLIT) |
Publikationstyp: | Zeitschriftenartikel |
Medienart: | Elektronische Ressource (online) Gedruckte Ressource |
Sprache: | Englisch |
ISSN: | 0112-1642, 1179-2035 |
DOI: | 10.1007/s40279-018-0879-3 |
Schlagworte: | |
Online Zugang: | |
Erfassungsnummer: | PU201812008820 |
Quelle: | BISp |
Abstract des Autors
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.