Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.274 | 0.165 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".