Notice bibliographique
Résumé
Worldwide, trauma is the leading cause of death in the first 4 decades of life, surpassed only by cancer and atherosclerosis in all age groups.1,2 For every death attributable to trauma, 3 patients survive but are permanently disabled.3,4 Globally, most traumas result from road traffic accidents (RTAs) and ranks in the top 10 causes of all-cause disability. By 2030, RTAs are anticipated to rank in the top 3 of all-cause disability behind depressive disorders and ischemic heart disease.5 Approximately 1.3 million people die each year on the world's roads, and between 20 and 50 million sustain nonfatal injuries.5 Accelerated urbanization and industrialization in many countries have led to an alarming increase in the rate of accidental injuries, crime, and violence.6 The number of vehicles has increased exponentially and has outpaced the development of adequate road space.7 Over the next 2 decades, India is expected to become the most populous country in the world.8 India has 1% of the motor vehicles in the world, but bears the burden of 6% of the global vehicular accidents.5 In China, motor vehicle accidents have increased from 50,000 to 50 million annually over the past 50 years.5 Although deaths from RTAs are on the decline in North America, they are increasing in many low-income and middle-income countries. January 1, 2011 marked the beginning of the United Nations and WHO collaboration for improving education, reducing mortality, and developing primary preventative strategies for road traffic injuries worldwide.9,10 The WHO's Global Road Traffic Safety Report recommended a major focus on research and interventions in developing nations given “over 90% of the world's fatalities on the roads occur in low-income and middle-income countries.”11 The Global Status Report on Road Safety (2009) was the first broad assessment of the road safety situation in 178 countries, using data drawn from a standardized survey.12 The international community must also play its part in halting and reversing the current global trend of increasing traffic injuries as an important health and development problem and by intensifying education and support. With over 100 governments sponsoring the UN's resolution toward road safety and injury prevention, the current supplement provides an opportunity to engage orthopaedic trauma surgeons worldwide to share their perceptions, provide opportunities for education, and propose evidence-based solutions. We have assembled an esteemed group of surgeons and researchers toward this global “call to action” with the objectives to summarize the decade of road traffic safety, explore the role of major trauma organizations over the next decade, examine regional trauma burden around the world, and propose evidence gaps and solutions for future engagement in orthopaedic surgery. The decade of road traffic safety demands that we think globally and act locally. This supplement informs critical issues relevant to the increased global burden of trauma, and also urges for “action” in both primary and secondary preventative strategies, knowledge translation and research to fill gaps in evidence.
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 | 0,002 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,006 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,274 | 0,165 |
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 ».