SPORTS UTILITY VEHICLES AND VULNERABLE ROAD USERS
Notice bibliographique
Résumé
We read with interest the article by Robertson,1 and we certainly agree that the sports utility vehicles (SUVs) pose an increased risk to small car occupants as well as vulnerable road users, such as pedestrians, cyclists, older persons, and children.2 In many developing countries, pedestrians, cyclists, rickshaw operators, and moped users represent the majority of road users. These countries rarely have the resources to physically separate such road users from car traffic. Many developing countries have no sidewalks or bicycle paths and those that do exist are heavily obstructed by trees, trash, drainage ditches, and vendors selling goods. Pedestrian injuries are a leading cause of global death and injury burden, accounting for 65% of the 1.2 million annual road deaths.3 Collisions between pedestrians and vehicles present a major challenge for public health, trauma and emergency medicine, and traffic safety professionals. Yet, in spite of the size of the pedestrian injury problem, research has concentrated almost exclusively on increasing the survival rates for vehicle occupants.4 A man waits for his identification wristband to be scanned during a mock evacuation drill May 23, 2006 in New Orleans, Louisiana. The two-day drill is being held in New Orleans and Baton Rouge in order to help the area prepare for the upcoming hurricane season. Photograph by Mario Tama. Courtesy of Getty Images. Recent US and United Arab Emirates studies have reported that increased numbers of SUVs and pickup trucks were associated with pedestrian deaths and a higher pedestrian injury severity score,5,6 and SUVs were 4 times as likely to be associated with fatal injury for the 5- to 9-year-old age group.7 Method of transportation is an important determinant of public health, affecting physical activity levels, social and economic opportunities, and general well-being.8 Extensive literature on the various health benefits of physical activity has illustrated the need to understand the determinants, risks, and barriers of physical activity. Vehicle danger is a disincentive to active lifestyles in that reducing the risks of fatal traffic injury for pedestrians and cyclists is an important part of any strategy to encourage walking and cycling to combat childhood obesity. Walking and cycling are essential modes of travel for many people living in low- and middle-income countries. Safe walking and cycling should be promoted, because their environmental, economic, and health benefits are significant.9 Potential reduction in the costs of treating heart disease if adults walked regularly has been calculated in the United States, saving US$5.6 billion annually if 10% of adults began a regular walking program.10 Safety standards for vehicle front-end construction, which would make vehicles less hazardous to pedestrians and cyclists, may be as important as standards that affect vehicle occupants; however, political obstacles have made such standards difficult to implement.1,2,5,7
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,004 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,002 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».