Mechanisms of Anterior Cruciate Ligament Injury in World Cup Alpine Skiing
Bibliographic record
Abstract
BACKGROUND: There is limited insight into the mechanisms of anterior cruciate ligament injuries in alpine skiing, particularly among professional ski racers. PURPOSE: This study was undertaken to qualitatively describe the mechanisms of anterior cruciate ligament injury in World Cup alpine skiing. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Twenty cases of anterior cruciate ligament injuries reported through the International Ski Federation Injury Surveillance System for 3 consecutive World Cup seasons (2006-2009) were obtained on video. Seven international experts in the field of skiing biomechanics and sports medicine related to alpine skiing performed visual analyses of each case to describe the injury mechanisms in detail (skiing situation, skier behavior, biomechanical characteristics). RESULTS: Three main categories of injury mechanisms were identified: slip-catch, landing back-weighted, and 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 caught abruptly 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 backward 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: Based on this video analysis of 20 injury situations, the main mechanism of anterior cruciate ligament injury in World Cup alpine skiing appeared to be a slip-catch situation where the outer ski catches the inside edge, 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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".