Motor Vehicle and Pedestrian Collisions: Burden of Severe Injury on Major Versus Neighborhood Roads
Bibliographic record
Abstract
OBJECTIVE: To determine whether the severity of injuries sustained by pedestrians involved in motor vehicle collisions varies by road type and age. METHODS: All police-reported pedestrian motor vehicle collisions in the city of Toronto, Canada, between January 1, 2000, and December 31, 2005, were analyzed. Geographic Information Systems software was used to determine whether the collisions occurred on major or neighborhood roads. Age-specific estimates of the burden of pedestrian collisions are presented. Odds ratios and 95 percent confidence intervals were calculated to examine age-specific relationships between injury severity and road type. A second analysis comparing the distribution of severe injury location between age groups was also performed. RESULTS: The majority of collisions involved adults (68%), although elderly pedestrians were overrepresented in fatal collisions (49%). Severe and fatal collisions involving working-age and elderly adult pedestrians were more likely on major roads. Odds of severe injury occurring on a major road were 1.36 (95% CI: 1.17-1.57) times higher for adults ages 18 to 64, and 1.55 (95% CI: 1.22-1.99) times higher for elderly aged 65+. By contrast, severe injuries among children were more common on neighborhood roads, with odds of severe injury on a major road of 0.64 (95% CI: 0.37-1.1) for children aged 5 to 9. Among children under 9, 64-67 percent of hospitalized or fatal injuries occurred on neighborhood roads, a marked difference from the distribution of such injuries in adults or the elderly, for whom only 29-30 percent of hospitalized or fatal injuries occurred on neighborhood roads (chi-square = 52.6, p < or =.001). CONCLUSIONS: Targeting interventions toward the adult pedestrian burden on major roads alone will not make child pedestrians safer. Pedestrian interventions specific to children and focused on neighborhood roads must be considered in urban centers like Toronto.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".