| Title |
An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport
|
|---|---|
| Published in |
Sports Medicine, March 2018
|
| DOI | 10.1007/s40279-018-0879-3 |
| Pubmed ID | |
| Authors |
Liam A. Toohey, Michael K. Drew, Lauren V. Fortington, Caroline F. Finch, Jill L. Cook |
| Abstract |
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. 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. 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. 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%. 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. |
X Demographics
The data shown below were collected from the profiles of 29 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 9 | 31% |
| United Kingdom | 4 | 14% |
| United States | 2 | 7% |
| Luxembourg | 1 | 3% |
| South Africa | 1 | 3% |
| Costa Rica | 1 | 3% |
| Spain | 1 | 3% |
| Unknown | 10 | 34% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 16 | 55% |
| Scientists | 11 | 38% |
| Science communicators (journalists, bloggers, editors) | 1 | 3% |
| Practitioners (doctors, other healthcare professionals) | 1 | 3% |
Mendeley readers
The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 61 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 15% |
| Student > Bachelor | 8 | 13% |
| Student > Master | 7 | 11% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Postgraduate | 3 | 5% |
| Other | 8 | 13% |
| Unknown | 20 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Sports and Recreations | 20 | 33% |
| Medicine and Dentistry | 11 | 18% |
| Nursing and Health Professions | 5 | 8% |
| Computer Science | 1 | 2% |
| Social Sciences | 1 | 2% |
| Other | 1 | 2% |
| Unknown | 22 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 May 2020.
All research outputs
#1,830,202
of 25,392,205 outputs
Outputs from Sports Medicine
#1,315
of 2,900 outputs
Outputs of similar age
#38,492
of 338,515 outputs
Outputs of similar age from Sports Medicine
#37
of 62 outputs
Altmetric has tracked 25,392,205 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,900 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 56.3. This one has gotten more attention than average, scoring higher than 54% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 338,515 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.