Helmet Use and Risk of Neck Injury in Skiers and Snowboarders
Bibliographic record
Abstract
In a case-control study, the authors examined the relation between helmet use and neck injury among Québec, Canada, skiers and snowboarders using 10 years of ski patrol data (1995-1996 to 2004-2005). Cases were defined as persons with any neck injury (n = 2,986), an isolated neck injury requiring ambulance evacuation (n = 522), or a cervical spine fracture or dislocation (n = 318). The control group included persons with non-head, non-neck injuries (n = 97,408) in an unmatched analysis. The authors also matched cases with controls injured at the same ski area, during the same activity (skiing vs. snowboarding), and during the same season. Helmet use was the primary exposure variable. For the unmatched analysis, the authors used unconditional logistic regression and adjusted for clustering by ski area and other covariates. They used conditional logistic regression for the matched analysis. Multiple imputation was used to address missing values. The adjusted odds ratio was 1.09 (95% confidence interval (CI): 0.95, 1.25) for any neck injury, 1.28 (95% CI: 0.96, 1.71) for isolated ambulance-evacuated neck injuries, and 1.02 (95% CI: 0.79, 1.31) for cervical spine fractures or dislocations. Similar results were found in the conditional logistic regression analysis and in analyses restricted to children under age 11 years. These results do not suggest that helmets increase the risk of neck injuries among skiers and snowboarders.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".