To develop a multivariable prediction model based on the differences in the facture pattern and demographic data between the rider and passenger Rider, Driver, Forensic, Motorcycle accident, Traffic, Fracture, Prediction model
Conditions
Interventions
A person who ride a motorcycle,A person who sit behind the rider
Diagnostic,Diagnostic
rider,passenger
Sponsors
lampang hospital
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: all record of motorcycle accident patients who experienced road traffic collision with another vehicle (including; collision with 2 wheels motor vehicle (V224, V225), collision with car or pickup truck (V234, V235), collision with heavy transport vehicle (V244, V245) and non-collision transport accident (V284, V285). The injury information codes were based on the International Statistical Classification of Diseases and Related Health Problems, 10th Revision (ICD-10). Only records with radiographic proof of at least one area of a bone fracture were included.
Exclusion criteria
Exclusion criteria: Exclusion criteria was patient with unidentified seated position including inconsistent patient reporting or patients unable to report due to the condition of injury.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| rider crossectional study full multivariable prediction model | — |
Secondary
| Measure | Time frame |
|---|---|
| passenger crossectional study full multivariable prediction model | — |
Countries
Thailand
Contacts
Public ContactGampon Kluakamkao
lampang hospital
Outcome results
None listed