Translating risk to preventable burden by estimating numbers of bicycling injuries preventable by separated infrastructure on a Toronto, Ontario corridor
Bibliographic record
Abstract
Objectives: Bicycling is a form of active transportation with a number of health benefits but carries a high risk of injury compared to other transportation modes. Safety intervention evaluations often produce results in the form of ratios, which can be difficult to communicate to policy-makers. The primary objective of this study was to estimate the number of bicycling injuries on an urban corridor preventable by separated bicycling infrastructure. Methods: Stakeholders identified a key corridor with multiple segments having bicycling infrastructure but most of the corridor lacking similar infrastructure. We counted bicyclist volume along this route and used secondary data to supplement counts missing due to COVID-19. We used two reference studies including local bicycling population to estimate benefit of separated bicycling infrastructure and applied this to a city-wide estimate of baseline risk of injury per kilometre bicycled, which used a combination of secondary data sources including police, health care and travel survey data. Finally, we adjusted baseline risk to account for increased bicyclist volume during and following the COVID-19 pandemic. Results: We estimated installation of fully separated cycle tracks along one Toronto corridor would prevent approximately 152.9 injuries and 0.9 fatalities over a 10-year period. Discussion: Our results underscore the benefits of separated bicycling infrastructure. We identify several caveats for our results, including the limitations of studies used to estimate relative risk of infrastructure. Our method could be adapted for use in other cities or along other corridors. Finally, we discuss the role of preventable burden estimates as a knowledge translation tool.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".