A Retrospective Analysis of Concurrent Pathology in ACL-Reconstructed Knees of Elite Alpine Ski Racers
Bibliographic record
Abstract
BACKGROUND: Anterior cruciate ligament (ACL) tear is the most frequent injury in alpine ski racing, and there is a high prevalence of ACL reinjury. Limited data exist on the concurrent pathology with primary ACL tears in elite alpine ski racers and the magnitude of injury progression after primary ACL reconstruction (ACLR). PURPOSE: To evaluate (1) the involvement of intra-articular and multiligament pathologies at the time of primary ACLR, (2) the subsequent progression in meniscal/chondral injuries, and (3) the occurrence of ACL reinjury in elite alpine ski racers. STUDY DESIGN: Case series; Level of evidence, 4. METHODS: Primary ACLR operative reports (n = 28) were obtained for 32 elite alpine ski racers along with the reports of 20 operative procedures that occurred subsequent to primary ACLR. Operative reports were evaluated to identify the presence/location of multiligament injury, meniscal tears, and chondral lesions. RESULTS: At the time of primary ACLR, a majority of knees (82%; 23/28) demonstrated concurrent injury compared with isolated ACL tears; 32% of knees sustained multiligament injuries (9/28), and 8 involved the ipsilateral medial collateral ligament (MCL). Of the ACL-injured knees, 54% had chondral lesions, of which 73% were sustained in the lateral knee compartment, and 82% of meniscal tears (14/17) were complex in nature. Bilateral ACL tears were seen in 22% of the participants, and 28% underwent ACL revision. In the case of ACL revision or future meniscal/chondral surgery, 60% of meniscal tears and 80% of chondral lesions had worsened since the time of primary ACLR. CONCLUSION: Concurrent injury was common in this group of elite ski racers. Primary ACL tears were typically accompanied by lateral compartment chondral lesions and complex meniscal tears that worsened over time. ACL/MCL tears were the most common multiligament injury pattern.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".