Fatal motorcycle crashes: a serious public health problem in Brazil
Bibliographic record
Abstract
INTRODUCTION: The numbers of two-wheel vehicles are growing across the world. In comparison to other vehicles, motorcycles are cheaper and thus represent a significant part of the automobile market. Both the mobility and speed are attractive factors to those who want to use them for work or leisure. Crashes involving motorcyclists have become an important issue, especially fatal ones. Specific severe injuries are responsible for the deaths. Defining them is necessary in order to offer better prevention and a more suitable medical approach. METHODS: All fatal motorcycle crashes between January 2001 and December 2009 in Campinas, Brazil, were analyzed in this study. Official data have been collected from police incident reports, hospitals' registers and autopsies. Both incidents and casualties were analyzed according to relevant variables. The Injury Severity Score (ISS) was calculated, describing the most potentially fatal injuries. RESULTS: There were 479 deaths; 90.8% were male; the mean age was 27.8 (range 0-73); 86.4% were conductors of the vehicles; blood alcohol was positive in 42.3%; 49.7% died at a hospital; 32.6% died at the scene; 26.1% of the accidents occurred at night, 69.1% were urban and 30.9% occurred on highways. The main causes of injury were collisions (63%) and falls (14%). The mean ISS was 38.5 (range 9-75). With regard to injuries, head trauma (67%) and thoracic trauma (40%) were the most common, followed by abdominal trauma (35%). Traumatic brain injury (67%) and hypovolemic shock (38%) were the most frequent causes of death. CONCLUSIONS: Alcohol was a significant factor in relation to the accidents. Head trauma was the most frequent and severe injury. Half of the victims died before receiving adequate medical attention, suggesting that prevention programs and laws should be implemented and applied in order to save future lives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".