Risk of death in crashes on Ontario’s highways
Bibliographic record
Abstract
BACKGROUND: Motor vehicle collisions (MVCs) that result in one or more fatalities on the 400-series Highways represent a serious public health problem in Ontario, and were estimated to have cost $11 billion in 2004. To date, no studies have examined risk factors for fatal MVCs on Ontario's 400 series highways.The investigate how demographic and environmental risk factors are associated with fatal MVCs on Ontario's 400-Series Highways. METHODS: Data were provided from the Ontario Ministry of Transport database, and included driver demographics, vehicle information, environmental descriptors, structural descriptors, as well as collision information (date and time), and severity of the collision. Multivariate analysis was used to identify factors significantly associated with the odds of dying in a collision. RESULTS: There were 53,526 vehicles involved in collisions from 2001 to 2006 included in our analysis. Results from the multivariate analysis suggest that collisions with older age and male drivers were associated with an increased risk of involving a fatality. Highway 405 and an undivided 2-way design proved to be the most fatal structural configurations. Collisions in the summer, Fridays, between 12 am-4 am, and in drifting snow conditions during the wintertime were also shown to have a significantly increased risk of fatality. CONCLUSION: Our results suggest that interventions to reduce deaths as a result of MVCs should focus on both driver-related and road-related modifications.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".