Anterior cruciate ligament injury/reinjury in alpine ski racing: a narrative review
Bibliographic record
Abstract
The purpose of the present review was to: 1) provide an overview of the current understanding on the epidemiology, etiology, risk factors, and prevention methods for anterior cruciate ligament (ACL) injury in alpine ski racing; and 2) provide an overview of what is known pertaining to ACL reinjury and return to sport after ACL injury in alpine ski racing. Given that most of the scientific studies on ACL injuries in alpine ski racing have been descriptive, and that very few studies contributed higher level scientific evidence, a nonsystematic narrative review was employed. Three scholarly databases were searched for articles on ACL injury or knee injury in alpine ski racing. Studies were classified according to their relevance in relation to epidemiology, etiology, risk factors, and return to sport/reinjury prevention. Alpine ski racers (skiers) were found to be at high risk for knee injuries, and ACL tears were the most frequent diagnosis. Three primary ACL injury mechanism were identified that involved tibial internal rotation and anteriorly directed shear forces from ski equipment and the environment. While trunk muscle strength imbalance and genetics were found to be predictive of ACL injuries in development-level skiers, there was limited scientific data on ACL injury risk factors among elite skiers. Based on expert opinion, research on injury risk factors should focus on equipment design, course settings/speed, and athlete factors (eg, fitness). While skiers seem to make a successful recovery following ACL injury, there may be persistent neuromuscular deficits. Future research efforts should be directed toward prospective studies on ACL injury/reinjury prevention in both male and female skiers and toward the effects of knee injury on long-term health outcomes, such as the early development of osteoarthritis. International collaborations may be necessary to generate sufficient statistical power for ACL injury/reinjury prevention research in alpine ski racing.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".