ENVIRONMENTAL DETERMINANTS OF BICYCLING INJURIES
Bibliographic record
Abstract
Background Identification of environmental risk factors for bicycling injuries could lead to improved safety and increased bicycling. Objectives To identify built environment characteristics associated with bicycling injuries. Methods Participants were recruited from seven emergency departments (ED) in Alberta, Canada. Cases were bicyclists struck by a motor-vehicle (MV) or with severe injuries (hospitalised). Controls were bicyclists who were not hit by a car or those seen and discharged from the ED, matched on day and time. Crash details were collected by interview and chart reviews. Environmental audits performed at injury locations captured path, roadway, safety, land use, and aesthetic characteristics. Logistic regression OR adjusted for age, sex, peak time, and bicyclist speed with 95% CI were estimated to relate injury risk to environmental characteristics. Results We audited 274 locations (70 case sites). A higher proportion of MV cases than controls (35.7% vs 11.3%) were commuting. Based on the unmatched analyses, the odds of a MV event were higher at locations with greater traffic volume (OR 3.5; 95% CI 1.4 to 8.9), intersections (OR 2.8; 95% CI 1.1 to 7.2), path obstructions (OR 2.6; 95% CI 1.1 to 5.9), and retail land use (OR 7.5; 95% CI 3.1 to 18.0). Locations with street lights (OR 0.4; 95% CI 0.2 to 0.9), high surveillance (OR 0.3; 95% CI 0.1 to 0.8), or good road condition (OR 0.4; 95% CI 0.2 to 0.9) reduced severe injury risk. Results were similar based on matched analyses. Significance Built environmental risk factors for bicyclist injury were identified and could be modified to increase safety and encourage more bicycling.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".