Route Infrastructure and the Risk of Injuries to Bicyclists: A Case-Crossover Study
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: We compared cycling injury risks of 14 route types and other route infrastructure features. METHODS: We recruited 690 city residents injured while cycling in Toronto or Vancouver, Canada. A case-crossover design compared route infrastructure at each injury site to that of a randomly selected control site from the same trip. RESULTS: Of 14 route types, cycle tracks had the lowest risk (adjusted odds ratio [OR] = 0.11; 95% confidence interval [CI] = 0.02, 0.54), about one ninth the risk of the reference: major streets with parked cars and no bike infrastructure. Risks on major streets were lower without parked cars (adjusted OR = 0.63; 95% CI = 0.41, 0.96) and with bike lanes (adjusted OR = 0.54; 95% CI = 0.29, 1.01). Local streets also had lower risks (adjusted OR = 0.51; 95% CI = 0.31, 0.84). Other infrastructure characteristics were associated with increased risks: streetcar or train tracks (adjusted OR = 3.0; 95% CI = 1.8, 5.1), downhill grades (adjusted OR = 2.3; 95% CI = 1.7, 3.1), and construction (adjusted OR = 1.9; 95% CI = 1.3, 2.9). CONCLUSIONS: The lower risks on quiet streets and with bike-specific infrastructure along busy streets support the route-design approach used in many northern European countries. Transportation infrastructure with lower bicycling injury risks merits public health support to reduce injuries and promote cycling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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