Bibliographic record
Abstract
Where appropriate we adjusted for personal characteristics and events at the time of injury.The small number of neck injuries, however, precluded control for more than a few covariates.As 13 of the participants with neck injuries who required evacuation by ambulance were in discordant matched sets, we did not consider further adjustment beyond that provided by the matching.Recall on the day of injury of a participant with head injuries was confined to a specific event for the case crossover analysis, 7 whereas recall on the previous outing may have been less accurate.If cases had over-reported previous helmet use then the protective effect of helmets would have been overestimated, but we found a consistent helmet effect between the matched case-control and case crossover analyses. Comparison with previous researchTo our knowledge only one other case-control study has been carried out to determine whether helmets protect skiers and snowboarders against head injuries.3 Those investigators evaluated combined head, face, and neck injuries in participants aged less than 13 years.After adjustment for activity, helmets were associated with a 43% reduction in the risk of head, face, and neck injuries, and no serious neck injury occurred in those using helmets.Our estimates for all age groups confirm the protective effect of helmets on head injuries found by this previous study.3 Although other investigators have examined the relation between helmet use and risk of neck injury in skiers and snowboarders, unlike us they did not examine this in isolation or did not report an effect estimate.3 8 We thank Genevieve Gore, Liam Gore, Helen Magdalinos, Gail Martin, and Anna Sampogna for their invaluable assistance; Nicole Marchand
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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 | 0.046 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.249 | 0.102 |
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".