Injuries to adult cyclists in Toronto and Vancouver: describing the circumstances as a first step towards injury prevention
Bibliographic record
Abstract
Introduction Bicycling is a sustainable mode of transportation with health benefits, but the risk of injury deters many people. We studied injured cyclists from two urban areas to characterise injury severity and mechanism. Methods Multicentre case-crossover study. Adult cyclists who visit emergency departments in three Toronto and two Vancouver hospitals are being recruited. Canadian Trauma and Acuity Score (CTAS) data are retrieved from hospital records. Descriptive data and comparisons of the first 300 injury events, 150 in each city, are presented. Results The median CTAS score was 3 (IQR: 3–4; n=228). Of the 300 cyclists studied, 27 (9.0%; 95% CI 5.8 to 12.2%) were admitted to hospital. Injury mechanism was classified as a collision in 213 cases (70.9%; 65.9–76.1%) or fall in 87 (29.1%; 23.9–34.1%). Collisions involved motor vehicles in 102 cases (34.1% of all events; 28.6–39.4%), streetcar/train tracks in 46 (15.4%; 10.9–19.0%), curbs/fences/barriers in 38 (12.7%; 8.3–15.7%), pedestrians/other cyclists in 14 (4.7%; 2.3–7.1%), potholes in 9 (3%; 1.1–4.9%) and animals in 3 (1%; 0–2.1%). Manoeuvres to avoid collisions resulted in 28 falls (9.3% of all events). The proportions of injuries involving motor vehicles were almost identical in the two cities, but the odds of an event involving dooring were higher in Toronto than Vancouver (OR 2.83, 95% CI 1.13 to 7.02). Toronto events were more likely to involve streetcar tracks (OR 19.6, 5.9 to 65.0) and less likely to involve pedestrians or cyclists (OR 0.33, 0.13 to 0.83) than those in Vancouver. Conclusions Injury circumstances and differences between cities suggest that transportation infrastructure and interactions with motorised and non-motorised traffic are important factors in cycling injuries.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".