Nonuse of bicycle helmets and risk of fatal head injury: a proportional mortality, case–control study
Bibliographic record
Abstract
BACKGROUND: The effectiveness of helmets at preventing cycling fatalities, a leading cause of death among young adults worldwide, is controversial, and safety regulations for cycling vary by jurisdiction. We sought to determine whether nonuse of helmets is associated with an increased risk of fatal head injury. METHODS: We used a case-control design involving 129 fatalities using data from a coroner's review of cycling deaths in Ontario, Canada, between 2006 and 2010. We defined cases as cyclists who died as a result of head injuries; we defined controls as cyclists who died as a result of other injuries. The exposure variable was nonuse of a bicycle helmet. RESULTS: Not wearing a helmet while cycling was associated with an increased risk of dying as a result of sustaining a head injury (adjusted odds ratio [OR] 3.1, 95% confidence interval [CI] 1.3-7.3). We saw the same relationship when we excluded people younger than 18 years from the analysis (adjusted OR 3.5, 95% CI 1.4-8.5) and when we used a more stringent case definition (i.e., only a head injury with no other substantial injuries; adjusted OR 3.6, 95% CI 1.2-10.2). INTERPRETATION: Not wearing a helmet while cycling is associated with an increased risk of sustaining a fatal head injury. Policy changes and educational programs that increase the use of helmets while cycling may prevent deaths.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".