The effectiveness of helmet wear in skiers and snowboarders: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it