Cyclist head and facial injury risk in relation to helmet fit: a case-control study
Bibliographic record
Abstract
INTRODUCTION: We examined the effect of bicycle helmet fit and position on head and facial injuries. METHODS: Cases were helmeted cyclists with a head (n=297) or facial (n=289) injury. Controls were helmeted cyclists with other injuries, excluding the neck. Participants were interviewed in seven Alberta emergency departments or by telephone; injury data were collected from charts. Missing values were imputed using chained equations and custom prediction imputation models. RESULTS: Compared with excellent helmet fit, those with poor fit had increased odds of head injury (odds ratio [OR] = 3.38, 95% confidence interval [CI]: 1.06-10.74). Compared with a helmet that stayed centred, those whose helmet tilted back (OR = 2.90, 95% CI: 1.54-5.47), shifted (OR = 1.91, 95% CI: 1.01-3.63) or came off (OR = 6.72, 95% CI: 2.86-15.82) had higher odds of head injury. A helmet that tilted back (OR = 4.81, 95% CI: 2.74-8.46), shifted (OR = 1.83, 95% CI: 1.04-3.19) or came off (OR = 3.31, 95% CI: 1.24-8.85) also increased the odds of facial injury. CONCLUSION: Our findings have implications for consumer and retail education programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".