The effectiveness of helmet wear in skiers and snowboarders: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To summarise the best available evidence to determine the impact of helmet use on head injuries, neck injuries and cervical spine injuries in skiers and snowboarders. DATA SOURCES: Relevant publications were identified through electronic searches of MEDLINE, PubMed, EMBASE, CINAHL and the Cochrane Library databases (1966-2009) in addition to manual reference checks of all included articles. REVIEW METHODS: 45 articles were identified through our systematic literature search. Of these, 10 studies met the inclusion criteria after two levels of screening. Two independent reviewers critically appraised the studies. Data were extracted on the primary outcomes of interest: head injury, neck injury and cervical spine injury. Studies were assessed for quality by the criteria of Downs and Black. RESULTS: Studies reviewed indicate that helmet wear reduces the risk of head injuries in skiing and snowboarding. Four case-control studies reported a reduction in the risk of head injury with helmet use ranging from 15% to 60%. Another cohort study found a significantly lower incidence of head injuries involving loss of consciousness in helmet users (p<0.05). The five remaining studies suggested a major protective effect of helmets by indicating that none or few of the head-injured and deceased participants wore a helmet. CONCLUSIONS: There is strong evidence to support the protective value of helmets in reducing the risk of head injuries in skiing and snowboarding. There is no good evidence to support the claim that the use of helmets leads to an increase risk of cervical spine injuries or neck injuries.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".