Characteristics of Injuries Sustained by Snowboarders in a Terrain Park
Bibliographic record
Abstract
OBJECTIVE: To determine injured body regions and injury type resulting from snowboarding on aerial and nonaerial terrain park features and the accuracy of ski patrol assessments compared with physician diagnoses. DESIGN: Case series study. SETTING: An Alberta terrain park during the 2008-2009 and 2009-2010 seasons. PATIENTS: There were 333 snowboarders injured on features (379 injuries). ASSESSMENT OF RISK FACTORS: Aerial or nonaerial terrain park feature used at injury, injured body region, injury type, and additional risk factors were recorded from ski patrol Accident Report Forms, emergency department medical records, and telephone interviews. MEASURES: Odds of injury to body regions and injury types on aerial versus nonaerial features were calculated using multinomial logistic regression. Accuracy of ski patrol injury assessments was examined through sensitivity, specificity, and kappa (κ) statistics. RESULTS: The wrist was the most commonly injured body region (20%), and fracture was the most common injury type (36%). Compared with the upper extremity, the odds of head/neck [odds ratio (OR), 2.58; 95% confidence interval (CI), 1.37-4.85] and trunk (OR, 3.65; 95% CI, 1.68-7.95) injuries were significantly greater on aerial features. There was no significant association between aerial versus nonaerial feature and injury type. The accuracy of ski patrol injury assessment was higher for injured body region (κ = 0.65; 95% CI, 0.54-0.75) than for injury type (κ = 0.29; 95% CI, 0.22-0.37). CONCLUSIONS: Snowboarders were significantly more likely to sustain head/neck or trunk injuries than upper extremity injuries on aerial features. Investigators should acknowledge potential misclassification when using ski patrol injury assessments.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".