ArticleSports medicine (Auckland, N.Z.)2018
An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport.
Article in Sports medicine (Auckland, N.Z.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Subsequent Physical Activity-Related Musculoskeletal Injuries in University Students: The Role of Body Composition, Training Weekly Load, and Physical Activity Intensity.Journal of clinical medicine · 2026Article
- Longitudinal cohort study on subsequent injury risk in professional football players in the Qatar Stars League: a probabilistic approach using basic learning.Biology of sport · 2026Article
- What is a recurrence? The onset, frequency and time loss impact of recurrent calf muscle strain injuries in elite male Australian football players over a decade.BMJ open sport & exercise medicine · 2025Article
- A Validated Injury Surveillance and Monitoring Tool for Fast Jet Aircrew: Translating Sports Medicine Paradigms to a Military Population.Sports medicine - open · 2022Article
- Recurrent and Subsequent Injuries in Professional and Elite Sport: a Systematic Review.Sports medicine - open · 2020Review
- International Olympic Committee consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS)).British journal of sports medicine · 2020Article
- International Olympic Committee Consensus Statement: Methods for Recording and Reporting of Epidemiological Data on Injury and Illness in Sports 2020 (Including the STROBE Extension for Sports Injury and Illness Surveillance (STROBE-SIIS)).Orthopaedic journal of sports medicine · 2020Article
- Lower limb MSK injuries among school-aged rugby and football players: a systematic review.BMJ open sport & exercise medicine · 2020Review
- Comparison of subsequent injury categorisation (SIC) models and their application in a sporting population.Injury epidemiology · 2019Article
- The Current State of Subjective Training Load Monitoring-a Practical Perspective and Call to Action.Sports medicine - open · 2018Article
- Recent Versus Old Previous Injury and Its Association with Running-Related Injuries During Competition by SeRUN® Running Profiles: a Cross-sectional Study.Sports medicine - open · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundAccounting 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.
objectivesThe 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.
methodsThe 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.
resultsAn 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%.
conclusionsThe 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.
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