Return to On-Snow Performance in Ski Racing After Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
BACKGROUND: The individual variation in on-snow performance outcomes after anterior cruciate ligament (ACL) reconstruction (ACLR) in elite alpine ski racers has not been reported and may be influenced by specific injury characteristics. PURPOSE: To report the performance statistics of elite ski racers before and after ACLR and to identify surgical and athlete-specific factors that may be associated with performance recovery. STUDY DESIGN: Descriptive epidemiological study. METHODS: International Ski and Snowboard Federation (FIS) points, FIS ranking, average placing, and average percentage behind the winning time in each race were calculated for 30 national and provincial team ski racers at 1 year before and 3 years after ACLR. Injury characteristics were obtained from operative reports and clinical notes. RESULTS: The mean age at the time of primary ACLR was 21.6 ± 3.5 years. Overall, 27 of 30 (90%) ski racers returned to the same preinjury competition level. Yet, only 16 of 30 (53%) improved in FIS points, and 13 of 30 (43%) improved in FIS ranking, in one of the speed or technical disciplines by 3 years after surgery. Of the skiers who improved in FIS points, 36% sustained multiligamentous injuries, 45% sustained meniscal tears, and 45% sustained chondral lesions. Of those who failed to improve in FIS points, 50% sustained multiligamentous injuries, 50% sustained meniscal tears, and 60% sustained chondral lesions. Meniscal tears and chondral lesions occurred mostly on the lateral side. The medial collateral ligament was involved in 8 of 9 multiligamentous injuries. CONCLUSION: These findings suggest significant individual variation in the recovery of on-snow performance of ski racers after ACLR, despite returning to the same competition circuit. The pattern of secondary injuries alongside primary ACL ruptures showed little association with improved performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".