The Classification and Analysis of 300 Cycling Crashes that Resulted in Visits to Hospital Emergency Departments in Toronto and Vancouver
Bibliographic record
Abstract
Although many benefits of cycling exist, the injuries often deter people from this sustainable mode of transportation. As part of the Bicyclists’ Injuries and Cycling Environment study, interviews were conducted with 300 injured cyclists who visited the emergency department of one of 5 hospitals in Toronto or Vancouver. This paper classifies the crashes based on their circumstances and analyzes selected characteristics with a particular interest in city and demographic comparisons. Crashes were broadly classified as collisions (72%) or falls (28%) and as involving motor-vehicles (48.3%) or not. Injured cyclists in Toronto more frequently collided with streetcar tracks (Odds Ratio: 21.0) or vehicle doors (OR: 3.96), and less frequently collided with pedestrians or animals (OR: 0.29) than those in Vancouver. In a multiple logistic regression model comparing the odds of a crash being a collision versus a fall, collisions were more common in Toronto (OR: 3.50) than Vancouver, on trips to work or school (OR: 4.66) than trips for other purposes, and for injured females (OR: 1.69) than injured males. In a second model, motor-vehicle involvement was found to be more common among injured cyclists less than 30 years old (OR: 2.00) than those who were older, and on trips to work or school (OR: 2.89) than for other purposes. The use of drugs or alcohol was not significantly related to the crash circumstances. Variations in crash circumstances between cities suggest that modification of infrastructure could improve safety and increase the number of cyclists.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".