Prediction model for autopsy diagnosis of driver and front passenger in fatal road traffic collisions

•It may be necessary to distinguish driver and passenger following fatalities due to road traffic collision.•The distribution of skin, visceral and bone injuries may be associated with driver or front seat passenger status.•Use of mathematical models based on injury pattern could help in such a dist...

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Veröffentlicht in:Forensic science international 2021-07, Vol.324, p.110853-110853, Article 110853
Hauptverfasser: Blandino, Alberto, Travaini, Guido, Rifiorito, Arianna, Piga, Maria Antonella, Casali, Michelangelo Bruno
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Travaini, Guido
Rifiorito, Arianna
Piga, Maria Antonella
Casali, Michelangelo Bruno
description •It may be necessary to distinguish driver and passenger following fatalities due to road traffic collision.•The distribution of skin, visceral and bone injuries may be associated with driver or front seat passenger status.•Use of mathematical models based on injury pattern could help in such a distinction.•A mathematical model is presented. Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. The process to distinguishing driver from front seat passenger following fatal motor vehicle collision may use multiple sources of information, including autopsy injury pattern analysis.
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Road traffic collisions (RTC) analysis is almost a daily activity in many autopsy room. Especially when analyzing an RTC with multiple occupants in the car, it can be necessary to distinguish driver from front seat passenger in order to provide the judicial authority with elements useful to understand and to prove who was driving, considering the criminal and civil responsabilities that may derive from it. Despite this, it is beyond doubt that there is enormous difficulty in providing such information. The aim of this paper is then to evaluate whether it is possible to differentiate driver and front seat passenger in case of fatal collisions using a mathematical model based on injury pattern alone. Autopsy reports concerning 90 drivers and 60 front-seat passengers were analyzed. Statistical analysis was used to detect injuries capable of discriminating between driver and passenger, considering skin, skeletal and visceral injuries. Results show that certain skin injuries, fractures and internal organ lesions are possibly associated with drivers and front seat passenger status and the overall injury pattern seems to be able to provide useful information. A mathematical model is presented. 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subjects Abdomen
Autopsies
Autopsy
Cameras
Collisions
Contusions
Crime
Criminal investigations
Criminal liability
Driver
Fatalities
Forensic pathology
Forensic science
Forensic sciences
Fractures
Front passenger
Injuries
Injury analysis
Injury pattern
Mathematical analysis
Mathematical model
Mathematical models
Medical diagnosis
Motor vehicles
Passengers
Pattern analysis
Prediction models
Skin
Skin injuries
Statistical analysis
Traffic
Traffic collision
title Prediction model for autopsy diagnosis of driver and front passenger in fatal road traffic collisions
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