The effect of helmets on the risk of head and neck injuries among skiers and snowboarders: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: The prevention of head injuries in alpine activities has focused on helmets. However, no systematic review has examined the effect of helmets on head and neck injuries among skiers and snowboarders. METHODS: We searched electronic databases, conference proceedings and reference lists using a combination of the key words "head injury or head trauma," "helmet" and "skiing or snowboarding." We included studies that used a control group; compared skiers or snowboarders with and without helmets; and measured at least one objectively quantified outcome (e.g., head injury, and neck or cervical injury). RESULTS: We included 10 case-control, 1 case-control/case-crossover and 1 cohort study in our analysis. The pooled odds ratio (OR) indicated that skiers and snowboarders with a helmet were significantly less likely than those without a helmet to have a head injury (OR 0.65, 95% confidence interval [CI] 0.55-0.79). The result was similar for studies that used controls without an injury (OR 0.61, 95% CI 0.36-0.92), those that used controls with an injury other than a head or neck injury (OR 0.63, 95% CI 0.52-0.80) and studies that included children under the age of 13 years (OR 0.41, 95% CI 0.27-0.59). Helmets were not associated with an increased risk of neck injury (OR 0.89, 95% CI 0.72-1.09). INTERPRETATION: Our findings show that helmets reduce the risk of head injury among skiers and snowboarders with no evidence of an increased risk of neck injury.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.053 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".