DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192
Traffic accidents produce high morbidity and mortality in several countries, including Brazil. The initial care to victims of accidents, by a specialized team, has tools for evaluating the severity of trauma, which guide the priorities. This study aimed to develop a decision model applied to pre...
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Universidade Estadual do Sudoeste da Bahia
2012-08-01
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doaj-e30a2344fc0d4179b567f0bea3f15b712020-11-24T23:08:00ZporUniversidade Estadual do Sudoeste da BahiaRevista Saúde.com1809-07611809-07612012-08-0192216DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192Rackynelly Alves Sarmento Soares0Ana Paula de Jesus Tomé Pereira1Ronei Marcos de Moraes2Rodrigo Pinheiro de Toledo Vianna3Universidade Federal da Paraíba - UFPBUniversidade Federal da Paraíba - UFPBUniversidade Federal da Paraíba - UFPBUniversidade Federal da Paraíba - UFPBTraffic accidents produce high morbidity and mortality in several countries, including Brazil. The initial care to victims of accidents, by a specialized team, has tools for evaluating the severity of trauma, which guide the priorities. This study aimed to develop a decision model applied to pre-hospital care, using the Abbreviated Injury Scale, to define the severity of the injury caused by the AT, as well to describe the features of accidents and their victims, occurred in Joao Pessoa, Paraiba. This is a descriptive epidemiological investigation, sectional, which analyzed all victims of traffic accidents attended by the SAMU 192, João Pessoa-PB, in January, April and June 2010. Data were collected in the medical regulation sheets of SAMU 192. Most of victims were male (76%), aged between 20 and 39 years (60%). Most injuries were classified as AIS1 (62.5%). The model of decision support implemented was Suporte a vitimas de acidente de trânsito atendidas pelo SAMU 192 the decision tree that managed to correctly classify 95.98% of the severity of injuries. By this model, it was possible to extract 29 rules of gravity classification of injury, which may be used for decisionmaking teams of the SAMU 192. http://www.uesb.br/revista/rsc/ojs/index.php/rsc/article/view/180/215Traffic accidentsclassificationabbreviated injury scaleDecision Support Techniquesdecision trees |
collection |
DOAJ |
language |
Portuguese |
format |
Article |
sources |
DOAJ |
author |
Rackynelly Alves Sarmento Soares Ana Paula de Jesus Tomé Pereira Ronei Marcos de Moraes Rodrigo Pinheiro de Toledo Vianna |
spellingShingle |
Rackynelly Alves Sarmento Soares Ana Paula de Jesus Tomé Pereira Ronei Marcos de Moraes Rodrigo Pinheiro de Toledo Vianna DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 Revista Saúde.com Traffic accidents classification abbreviated injury scale Decision Support Techniques decision trees |
author_facet |
Rackynelly Alves Sarmento Soares Ana Paula de Jesus Tomé Pereira Ronei Marcos de Moraes Rodrigo Pinheiro de Toledo Vianna |
author_sort |
Rackynelly Alves Sarmento Soares |
title |
DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 |
title_short |
DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 |
title_full |
DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 |
title_fullStr |
DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 |
title_full_unstemmed |
DECISION MODEL SUPPORT OF SEVERITY OF INJURY TRAFFIC ACCIDENT VICTIMS CARE BY SAMU 192 |
title_sort |
decision model support of severity of injury traffic accident victims care by samu 192 |
publisher |
Universidade Estadual do Sudoeste da Bahia |
series |
Revista Saúde.com |
issn |
1809-0761 1809-0761 |
publishDate |
2012-08-01 |
description |
Traffic accidents produce high morbidity and mortality in several
countries, including Brazil. The initial care to victims of accidents,
by a specialized team, has tools for evaluating the severity of
trauma, which guide the priorities. This study aimed to develop a
decision model applied to pre-hospital care, using the Abbreviated
Injury Scale, to define the severity of the injury caused by the AT,
as well to describe the features of accidents and their victims,
occurred in Joao Pessoa, Paraiba. This is a descriptive
epidemiological investigation, sectional, which analyzed all victims
of traffic accidents attended by the SAMU 192, João Pessoa-PB, in January, April and June 2010. Data were collected in the medical regulation sheets of SAMU 192. Most of victims were male (76%), aged between 20 and 39 years (60%). Most injuries were classified as AIS1 (62.5%). The model of decision support implemented was Suporte a vitimas de acidente de trânsito atendidas pelo SAMU 192 the decision tree that managed to correctly classify 95.98% of the severity of injuries. By this model, it was possible to extract 29 rules of gravity classification of injury, which may be used for decisionmaking teams of the SAMU 192.
|
topic |
Traffic accidents classification abbreviated injury scale Decision Support Techniques decision trees |
url |
http://www.uesb.br/revista/rsc/ojs/index.php/rsc/article/view/180/215 |
work_keys_str_mv |
AT rackynellyalvessarmentosoares decisionmodelsupportofseverityofinjurytrafficaccidentvictimscarebysamu192 AT anapauladejesustomepereira decisionmodelsupportofseverityofinjurytrafficaccidentvictimscarebysamu192 AT roneimarcosdemoraes decisionmodelsupportofseverityofinjurytrafficaccidentvictimscarebysamu192 AT rodrigopinheirodetoledovianna decisionmodelsupportofseverityofinjurytrafficaccidentvictimscarebysamu192 |
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1725615916994002944 |