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Record W2062091896 · doi:10.1097/bot.0000000000000107

Status of Road Safety and Injury Burden

2014· article· en· W2062091896 on OpenAlexaff
Vinícius Ynoe de Moraes, Katelyn Godin, Fernando Baldy dos Reis, João Carlos Belloti, Mohit Bhandari

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2014
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineOccupational safety and healthInjury preventionPoison controlEnvironmental healthMedical emergencyPathology

Abstract

fetched live from OpenAlex

THE INCIDENCE OF RTAS AND THE ASSOCIATED FATALITIES Within the past 3 decades, there have been 1 million Road Traffic Accidents (RTAs) mortalities in Brazil.1 Recent data indicate that 150,000 individuals die or sustain serious injuries because of these accidents annually.2 In Brazil, pedestrians, motorcyclists, and vehicle conductors are greatly overrepresented among accident victims. Most notably, the last decade witnessed a striking increase of motorcycle accidents, resulting in a 7-fold increase in mortality rates (0.09–0.76 per million individuals) when compared with a more stable trend in the vehicle (0.46–0.61 per million individuals) and a decrease in the pedestrian deaths (15.7–6.5 per million individuals).2 In fact, half of all the Road Traffic Accidents in Brazil are now attributed to motorcycles. The high number of Road Traffic Accidents in Brazil can be in part explained by the increased vehicle fleet, which rose between 1.3- and 1.6-fold over the last decade. Motorcycles comprise nearly 1/5 of the total vehicle fleet, which represented a 75% increase between 2001 and 2005.2 DISEASE BURDEN DUE TO RTAS: THE ORTHOPAEDIC TRAUMA PERSPECTIVE There is a paucity of reliable prospective data from orthopaedic trauma centers on Road Traffic Accidents in Brazil. As an example, Brazilian hospital records indicate that there are 120,000 admissions each year due to Road Traffic Accidents. Of these admissions, 1500 resulted in limb amputations and 500 involved severe spine injuries, both of which result in very poor long-term health and social outcomes for victims.1 Numerous studies suggest that motorcyclists are the most susceptible to significant trauma, including lower extremity fractures, which are open injuries 50% of the time. Furthermore, 12% of motorcyclists had consumed alcohol when the injury occurred.3,4 COSTS OF RTAS IN BRAZIL Within 2012, Road Traffic Accidents injuries directly cost the health care system approximately 110 million dollars.2 Although these costs are quite staggering, it is important to highlight the significant indirect costs, which actually comprise a greater proportion of the total economic burden of Road Traffic Accidents in Brazil.5,6 Notably, workforce loss comprises 42.8% of the economic burden of Road Traffic Accidents, whereas material (vehicle) losses are associated with 28.8%. Direct costs through health care expenditures represent 13.3% of the economic burden of Road Traffic Accidents in Brazil. However, these data are likely underestimates because minor injuries are excluded from this analysis and the investigators did not account for the private health system, which provides 20% of the health care for Brazilians.1 NATIONAL RESPONSE TO THE RTA BURDEN IN BRAZIL: DRINKING AND DRIVING Drinking and driving has been prohibited in Brazil since 2008. Subsequent to this, studies revealed an important decrease in injury patterns.6–8 Most recently, in January 2013, Brazil adopted a zero-tolerance “dry law” regarding the consumption of alcohol while driving and has set the maximum blood alcohol level at 0.2 g/L.7,8 The penalty for exceeding this limit is proportional to how far above the limit the driver is and can include suspension or prison. FUTURE OUTLOOK AND INITIATIVES Despite the success of these initiatives, RTAs remain a priority area for intervention within Brazil. Indeed, 2012 data demonstrate that given the increase in the number of drivers and vehicles on the roads in Brazil, the number of RTAs has not decreased as hoped; instead, it has remained constant.9,10 For the orthopaedic perspective, efforts to reduce RTAs should include public education, development of an efficient emergency care system, and strict transit policies. Road safety and infrastructure that support safer roads and the protection of pedestrians and cyclists are critical steps to improvement.11

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.070
Threshold uncertainty score0.140

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.002

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.

Opus teacher head0.018
GPT teacher head0.312
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2014
Admission routes1
Has abstractyes

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