Bicycle-related head injury rate in Canada over the past 10 years
Bibliographic record
Abstract
Introduction Bicycle use is common among Canadian children. Legislation to promote bicycle helmet use varies by province and previous publications examining the association between legislation and head injuries are now outdated due to changes in legislation status over time. Purpose To determine bicycle-related injury rates (head and other injuries) in Canada over the past 10 years using hospitalisation data as well to compare those provinces with and without legislation. Methods Childhood bicycle related injuries were extracted from the Canadian Institute for Health Information hospital admissions database. Injury rates were calculated for each province for children aged 5–19 using 2001 census data. Results During the 10 years there were 23 685 hospital admissions due to bicycle-related injuries among Canadian children age 5–19 (76% men and 24% women). A total of 22% of the children sustained a head injury while 78% had other injuries due to a bicycle incident. 22% of men and 21% of women incurred a head injury. The injury rate varied by age group and by provincial legislation status. In general the rate of head injuries is declining, but this is not consistent across the country, nor is it attributable to legislation as some provinces with legislation experienced a decline while others did not. Conclusion Although bicycle-related injuries are generally declining, this decline is not consistent, nor is it clearly associated with helmet laws.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".