Effectiveness of helmets in skiers and snowboarders: case-control and case crossover study
Bibliographic record
Abstract
OBJECTIVE: To determine the effect of helmets on the risk of head and neck injuries in skiers and snowboarders. DESIGN: Matched case-control and case crossover study. SETTING: 19 ski areas in Quebec, Canada, November 2001 to April 2002. PARTICIPANTS: 1082 skiers and snowboarders (cases) with head and neck injuries reported by the ski patrol and 3295 skiers and snowboarders (controls) with non-head or non-neck injuries matched to cases at each hill. MAIN OUTCOME MEASURES: Estimates of matched odds ratios for the effect of helmet use on the risk of any head or neck injury and for people requiring evacuation by ambulance. RESULTS: The adjusted odds ratio for helmet use in participants with any head injury was 0.71 (95% confidence interval 0.55 to 0.92), indicating a 29% reduction in the risk of head injury. For participants who required evacuation by ambulance for head injuries, the adjusted odds ratio for helmet use was 0.44 (0.24 to 0.81). Similar results occurred with the case crossover design (odds ratio 0.43, 0.09 to 1.83). The adjusted odds ratio for helmet use for participants with any neck injury was 0.62 (0.33 to 1.19) and for participants who required evacuation by ambulance for neck injuries it was 1.29 (0.41 to 4.04). CONCLUSIONS: Helmets protect skiers and snowboarders against head injuries. We cannot rule out the possibility of an increased risk of neck injury with helmet use, but the estimates on which this assumption is based are imprecise.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".