Trends in Pediatric and Adult Bicycling Deaths Before and After Passage of a Bicycle Helmet Law
Bibliographic record
Abstract
OBJECTIVES: The goals were to examine bicycle-related mortality rates in Ontario, Canada, from 1991 to 2002 among bicyclists 1 to 15 years of age and 16 years of age through adulthood and to determine the effect of legislation (introduced in October 1995 for bicyclists <18 years of age) on mortality rates. METHODS: The average numbers of deaths per year and mortality rates per 100000 person-years for the prelegislation and postlegislation periods, and the percentage changes, were calculated for each of the 2 age groups (1-15 years and >/=16 years). Differences before and after legislation in the 2 age groups were modeled in a time series analysis. RESULTS: There were 362 bicycle-related deaths in the 12-year period (1-15 years: 107 deaths; >/=16 years: 255 deaths). For bicyclists 1 to 15 years of age, the average number of deaths per year decreased 52%, the mortality rate per 100000 person-years decreased 55%, and the time series analysis demonstrated a significant reduction in deaths after legislation. The estimated change in the number of deaths per month was -0.59 deaths per month. For bicyclists >/=16 years of age, there were only slight changes in the average number of deaths per year and the mortality rate per 100000 person-years, and the time series analysis demonstrated no significant change in deaths after legislation. CONCLUSIONS: The bicycle-related mortality rate in children 1 to 15 years of age has decreased significantly, which may be attributable in part to helmet legislation. A similar reduction for bicyclists 16 years of age through adulthood was not identified. These findings support promotion of helmet use, enforcement of the existing law, and extension of the law to adult bicyclists.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".