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Mendeley readers
Attention Score in Context
| Title |
Using matrix frame to present road traffic injury pattern
|
|---|---|
| Published in |
Injury Epidemiology, April 2018
|
| DOI | 10.1186/s40621-018-0154-y |
| Pubmed ID | |
| Authors |
Chien-Hsing Wang, Wan-Hua Hsieh, Fu-Wen Liang, Tsung-Hsueh Lu |
| Abstract |
Although many epidemiological studies have presented road traffic injuries (RTIs) according to the victim's mode of transport, very few have mentioned the mode of transport of the victim's counterparts. We sought to use matrix frame to present the pattern of RTIs based on the International Classification of Diseases, Tenth Revision (ICD-10) codes. Patients admitted to Hualien Tzu Chi Hospital, Taiwan, for RTIs from January 1, 2013 to December 31, 2016 were included. The numbers and proportions of various crash types of RTIs were presented using a matrix frame. The row margin of the matrix is the second character of ICD-10 codes V00-V79 (victim's mode of transport), and the column margin of the matrix is the third character of ICD-10 codes V00-V79 (mode of transport of victim's counterpart), constituting a 80-cell grid. In total, 2727 patients were included. The cell with the highest proportion in the matrix grid was ICD-10 code V23 "motorcycle rider injured in collision with car, pick-up truck or van" (27.0%, 737/2727), followed by that of V27 "motorcycle rider injured in collision with fixed or stationary object" (12.5%, 342/2727) and V28 "motorcycle rider injured in noncollision transport accident" (12.2%, 334/2727). The matrix pattern of RTIs differed with sex and age. By using the matrix frame, we can easily understand the RTI pattern for different demographic groups and identify the priority crash types. |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Postgraduate | 2 | 50% |
| Lecturer | 1 | 25% |
| Student > Doctoral Student | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Nursing and Health Professions | 2 | 50% |
| Computer Science | 1 | 25% |
| Engineering | 1 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 23 April 2018.
All research outputs
#5,815,818
of 23,045,021 outputs
Outputs from Injury Epidemiology
#165
of 328 outputs
Outputs of similar age
#100,703
of 326,557 outputs
Outputs of similar age from Injury Epidemiology
#8
of 16 outputs
Altmetric has tracked 23,045,021 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 328 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.1. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 326,557 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.