Évaluation des inégalités sociales de santé des traumatisés de la route à Ouagadougou au Burkina Faso
Bibliographic record
Abstract
INTRODUCTION: Each year, more than 1.2 million deaths in the world are due to road accidents. It is the leading cause of mortality in young people between the ages of 15 and 29 years. Road accidents and associated injuries have a major impact on health and development. Many international reports have evaluated the mortality and morbidity related to road accidents, but these studies are based on limited data, often with limited robustness. Based on a study conducted in Ouagadougou, the capital of Burkina Faso, to estimate the mortality and morbidity of road accident victims, the objective of this article was to evaluate social, spatial and health inequalities of populations killed or injured as a result of a road accident. METHODS: Between February and July 2015, all road accidents were recorded by policemen using a mobile telephone data collection system, including geolocation of accidents. Three quantitative and prospective survey phases were then performed on injured patients admitted to Yalgado Ouedraogo hospital, the reference centre for these patients. RESULTS: A total of 1,867 emergency department admissions were reported. The majority (95%) of road accident victims were vulnerable users. More than 30% of accidents occurred in one central quarter and two peripheral quarters. The mean management time as 1 hour 3 minutes for victims rescued by firemen and 3 hours 10 minutes for those who attended the emergency department on their own. The mean total cost of management was 126,799 CFA francs (€193) [400-2,000,000 CFA francs]. DISCUSSION: These results identify possible actions designed to reduce road accident injuries and their consequences. They demonstrate that the creation of surveillance systems common to police forces, rescue and health care services are essential to produce convincing data.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".