OA2064. Data limitations in the epidemiology of injuries to vulnerable road users in four Canadian cities
Bibliographic record
Abstract
Abstract Background Injuries to pedestrians and bicyclists are frequent in cities, particularly in car-centric cities in North America. However, the need for a reduction in automotive transportation and concurrent increase in walking and bicycling is essential for sustainability. Methods This study examines what information is available to Canadian municipalities, and what is needed to make evidence-based decisions on how to make the built environment safer for walking and bicycling. This includes different ways to count injuries, and evaluations of interventions to reduce speed, including automated speed enforcement. Results A summary of data from 4 Canadian cities will identify what we know (pedestrian and cyclist injury rates, effectiveness of speed reduction interventions) and where there are gaps in the data (measures of exposure to risk). For example, an comparison of police data to Emergency Department data indicates that police data capture only 8% of bicyclist injuries and 48% of pedestrian injuries. Speed humps are associated with a 22% reduction in pedestrian injuries but are four times more common in high income neighbourhoods compared to low-income neighbourhoods. Automated speed enforcement is associated with a reduction in speed, particularly cars travelling 85th percentile higher than the posted speed limit. Conclusion This presentation will provide insight into the effectiveness of interventions and the need for better data around pedestrians and cyclists, particularly their exposure to risk. Key messages • Municipalities need data and evidence to make decisions about road safety, including injury surveillance. • There is evidence that some interventions work well to reduce injuries to pedestrians and bicyclists, but there are also gaps in the data needed to make decisions. Topic Vulnerable Road Users, Research Gaps, Surveillance.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.021 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".