Translating risk to preventable burden by estimating numbers of bicycling injuries preventable by separated infrastructure on a Toronto, Ontario corridor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it