Child and adolescent bicycling injuries involving motor vehicle collisions
Bibliographic record
Abstract
BACKGROUND: Bicycle-related injuries are among the most common recreational injuries for children in Canada; moreover, bicycle-motor vehicle collisions often result in serious injuries. This study seeks to examine environmental, motorist, and bicyclist characteristics of bicycle-motor vehicle collisions that resulted in police reported severe injuries in youth (< 18 years old) bicyclists, in Alberta, Canada. FINDINGS: Using Calgary and Edmonton police collision reports, 423 youth bicycle-motor vehicle collisions were identified from 2010 to 2014. Forty-three (10.2%) of these collisions resulted in major/fatal (severe) injuries. These severe injury cases were compared with the 380 youth bicycle-motor vehicle collisions resulting in minor or no injury (controls) using classification tree and logistic regression analyses. There were no driver or bicyclist characteristics with a significant effect on the odds of severe injury to youth bicyclists; however, lower odds were found on each of: divided roads with no barrier (aOR = 0.36; 95% CI: 0.13-0.97) or during peak traffic time (aOR = 0.44; 95% CI: 0.16-0.99). CONCLUSION: Personal and environment characteristics should be considered in future research and interventions focused on reducing severe youth bicycle-motor vehicle collision injuries.
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.
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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".