The location of child cyclist versus motor vehicle collisions in an urban environment
Bibliographic record
Abstract
Background Cycling is a popular activity for children. Cyclists are disproportionately represented in motor collisions, and these collisions are frequently severe. This analysis was conducted to determine the age-specific variation in location of cyclist versus motor vehicle collision in children ages 1–17 years, in Toronto, Canada in order to identify appropriate prevention strategies. Methods All police-reported cyclist-motor vehicle collisions involving children ages 1–17 between 1 January 2000 and 31 December 2005 were included. Age-specific ORs were calculated to compare differences in collision locations. Geographic Information System software was used to identify major versus neighbourhood roads. Results There were 1325 police-reported collisions involving child cyclists with the majority (57%) involving 13–17 year olds. Collision rates were consistently higher in teenagers compared to younger children. Children ages 9–12 had almost twice and children ages 13–17 almost four times greater odds of collision on major roads as compared with 5–8-year-old children. Children ages 9–12 had a three times and children ages 13–17 had a four times greater odds of collisions at intersections (vs midblock) compared with 5–8-year-old children. Conclusions Younger children (ages 5–8) require more options for safe off-road cycling in their neighbourhoods as they generally are involved in collisions on smaller neighbourhood roads and in midblock locations. Older children (ages 9–17) require training in order to learn to safely negotiate intersections and vehicular traffic on larger roadways. It is essential to consider the age of children in order to plan successful strategies to encourage safe cycling.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".