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Enregistrement W2053936225 · doi:10.1080/15389588.2012.711498

Child and Youth Traffic-Related Injuries: Use of a Trauma Registry to Identify Priorities for Prevention in the United Arab Emirates

2013· article· en· W2053936225 sur OpenAlexaff
Michal Grivna, Peter Barss, Cristina Stănculescu, Hani O. Eid, Fikri M. Abu‐Zidan

Notice bibliographique

RevueTraffic Injury Prevention · 2013
Typearticle
Langueen
DomaineEngineering
ThématiqueTraffic and Road Safety
Établissements canadiensMcGill UniversityUniversity of British ColumbiaInterior Health
Organismes subventionnairesUtah Agricultural Experiment Station
Mots-clésMedicineInjury preventionOccupational safety and healthAbbreviated Injury ScalePoison controlSuicide preventionIncidence (geometry)Human factors and ergonomicsMedical emergencyInjury Severity ScorePediatrics

Résumé

récupéré en direct d'OpenAlex

OBJECTIVE: Traffic-related injuries are the main cause of death during childhood and youth in the United Arab Emirates (UAE), use of safety restraints by citizens is uncommon, rollovers are frequent, and current legislation does not protect rear-seat occupants. Because little was known about the circumstances of hospitalizations for traffic injuries to guide prevention, a trauma registry was used to assess causes and determinants for traffic-related injuries during childhood and youth (<19 years) and its value for prevention. METHODS: One hundred ninety-three children and youth with traffic injuries were admitted for more than 24 h at surgical wards of the main trauma hospital in the Al-Ain region during a 36-month period (2003-2006). Injuries were analyzed by age, nationality, road user and vehicle types, severity, anatomical region, and the presence of head injury using Injury Severity Scores (ISS) and the Abbreviated Injury Scale (AIS). RESULTS: Traffic injuries represented 40 percent (n = 193) of injuries to 0- to 19-year-olds, followed by falls (39 percent). Among 15- to 19-year-olds, who accounted for 46 percent of child and youth victims, the incidence was 150/100,000 person years, compared to an incidence of 15 to 51 for younger age groups. Overall, 53 percent were vehicle occupants, 23 percent were pedestrians, 14 percent were bicyclists, 6 percent were motorcyclists, with 4 percent other. The ratio of male-to-female victims was 6.7:1; for drivers it was 33:0; and for pedestrians, bicyclists, and motorcyclists it was between 10:1 and 12:1; injured females were mainly rear-seat passengers and the male: female ratio was 1.4:1. Seventy-one percent of pedestrians were ≤9 years old. Although the ratio of UAE children to foreign children was estimated at 0.7:1 in the community, 58 percent of the injured were UAE citizens. The ratio of injured UAE: non-UAE citizens was 1.4:1 overall but 5.6:1 for drivers and 4.5:1 for motorcyclists. Forty-one percent of citizens were injured in 4-wheel drive sport utility vehicles compared to 13 percent of non-citizens. Head injuries occurred in 68 percent of vehicle occupants and 51 percent of nonoccupants, with AIS ≥ 3 injuries in 23 percent of occupants and 26 percent of nonoccupants. Sixty-seven percent of rear occupants had head injuries. CONCLUSIONS: Male drivers and vulnerable road users were at an unusually high risk relative to females. A relatively high frequency of traffic-related head injuries among UAE children and youth, including rear-seat passengers and other vehicle occupants, suggests that considerable preventable morbidity is associated with nonuse of safety restraints and/or other factors such as excess speed and rollovers of 4-wheel drive vehicles. Trauma registries can be useful for prevention; inclusion of data on safety restraints and helmet use by road user type is essential.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,657
Score d'incertitude au seuil0,906

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,267
Écart entre enseignants0,245 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations26
Publié2013
Routes d'admission1
Résumé présentoui

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