Mechanisms of anterior cruciate ligament injury in world cup alpine skiing: a systematic video analysis of 20 cases
Bibliographic record
Abstract
Background We have limited insight into the mechanisms of anterior cruciate ligament (ACL) injuries in alpine skiing, particularly among professional ski racers. Objective To describe the mechanisms of ACL injury in World Cup alpine skiing. Design Descriptive video analysis. Setting World Cup alpine skiing. Methods 20 cases of ACL injuries reported through the International Ski Federation Injury Surveillance System for three consecutive World Cup seasons (2006–2009) were obtained on video. Seven international experts performed visual analyses of each case to describe the injury mechanisms in detail (skiing situation, skier behaviour, biomechanical characteristics). Results Three main categories of injury mechanisms were identified: the slip-catch, landing back-weighted and the dynamic snowplow. The slip-catch mechanism accounted for half of the cases (n=10), and all these injuries occurred during turning, without or before falling. The skier lost pressure on the outer ski, and while extending the outer knee to regain grip, the inside edge of the outer ski abruptly caught in the snow, forcing the knee into internal rotation and valgus. The same loading pattern was observed for the dynamic snowplow (n=3). The landing back-weighted category included cases (n=4) where the skier was out of balance backwards in-flight after a jump and landed on the ski tails with nearly extended knees. The suggested loading mechanism was a combination of tibiofemoral compression, boot induced anterior drawer and quadriceps anterior drawer. Conclusion A consistent pattern was observed where the main mechanism of ACL injury in World Cup alpine skiing appeared to be a slip-catch situation where the outer ski suddenly catches the inside edge, abruptly forcing the outer knee into internal rotation and valgus. A similar loading pattern was observed for the dynamic snowplow. Injury prevention efforts should focus on the slip-catch mechanism and the dynamic snowplow.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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 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".