RISK FACTORS FOR BODY REGION SPECIFIC INJURIES IN SKIERS AND SNOWBOARDERS
Bibliographic record
Abstract
Background Little is known about risk factors (e.g., wrist guards, binding adjustment, equipment ownership) for body region specific skiing/snowboarding injuries. Objective Identify risk factors for body region specific injuries and severe injuries in skiers/snowboarders. Design Case-control study using emergency department (ED) interviews, ski patrol Accident Report Forms (ARF), and ski area interviews. Severe injury cases presented to the ED. Non-severe injury cases presented to the ski patrol. Controls were uninjured skiers/snowboarders. Setting An urban ski area and 2 EDs in Alberta, Canada. Participants Injured skiers/snowboarders who saw the ski patrol (n=552) or went to one of the study EDs (n=192). Controls were interviewed at the ski area (n=914). Risk factor assessment Risk factor data were collected by interview and from ARFs. Injury outcomes were determined from the ED chart or ARF. Main outcome measurements Body region categories were: lower extremities, upper extremities, trunk, and head/neck. Odds ratios (OR) with 95% confidence intervals (CI) were calculated for each body region by case group. Intrinsic risk factors (age, sex, activity, ability) will be included in the models. Results Preliminary results for non-severe cases indicate that the odds of injury for all body regions were lower for those 12+ years old compared with younger participants (OR range 0.07; 95% CI 0.04–0.11 to 0.42; 95% CI 0.24–0.73) and higher for beginners (OR range 1.79; 95% CI 1.01–3.15 to 4.97; 95% CI 2.97–8.32). Overcast weather reduced the odds of any injury (OR range 0.06; 95% CI 0.02–0.18 to 0.44; 95% CI 0.2–0.98), as did increased run difficulty (OR range 0.06; 95% CI 0.02–0.17 to 0.19; 95% CI 0.0–0.54). Skiers had lower odds of upper body injury (ARF OR 0.27; 95% CI 0.2–0.38 & ED OR 0.33; 95% CI 0.21–0.51) compared with snowboarders. Conclusions These results will inform the development of an injury prevention program for skiers/snowboarders targeting modifiable risk factors.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".