Ski and snowboard school programs: Injury surveillance and risk factors for grade‐specific injury
Bibliographic record
Abstract
The objective of our study was to evaluate incidence rates and profile of school program ski and snowboard-related injuries by school grade group using a historical cohort design. Injuries were identified via Accident Report Forms completed by ski patrollers. Severe injury was defined as those with ambulance evacuation or recommending patient transport to hospital. Poisson regression analysis was used to examine the school grade group-specific injury rates adjusting for risk factors (sex, activity, ability, and socioeconomic status) and accounting for the effect of clustering by school. Forty of 107 (37%) injuries reported were severe. Adolescents (grades 7-12) had higher crude injury rates (91 of 10 000 student-days) than children (grades 1-3: 25 of 10 000 student-days; grades 4-6: 65 of 10 000 student-days). Those in grades 1-3 had no severe injuries. Although the rate of injury was lower in grades 1-3, there were no statistically significant grade group differences in adjusted analyses. Snowboarders had a higher rate of injury compared with skiers, while higher ability level was protective. Participants in grades 1-3 had the lowest crude and adjusted injury rates. Students in grades 7-12 had the highest rate of overall and severe injuries. These results will inform evidence-based guidelines for school ski/snowboard program participation by school-aged children.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".