Monitoring the Return to Sport Transition After ACL Injury: An Alpine Ski Racing Case Study
Bibliographic record
Abstract
Alpine ski racing is an extreme sport and ski racers are at high risk for ACL injury. ACL injury impairs neuromuscular function and psychological readiness putting alpine skiers with ACL injury at high risk for ACL reinjury. Consequently, return to sport training and testing protocols are recommended to safeguard ACL injured athletes against reinjury. The aim of this paper was to present a real-world example of a return to sport training plan for a female elite alpine ski racer who sustained an ACL injury that was supported by an interdisciplinary performance team (IPT) alongside neuromuscular testing and athlete monitoring. A multi-faceted return to sport training plan was developed by the IPT shortly after the injury event that accounted for the logistics, healing, psychological readiness, functional milestones, work capacity and progression to support the return to sport/return to performance transition. Neuromuscular testing was conducted at several timepoints post-injury. Importantly, numerous pre-injury tests provided a baseline for comparison throughout the recovery process. Movement competencies and neuromuscular function were assessed, including an evaluation of muscle properties (e.g., the force-velocity and force-length relationships) to assist the IPT in pinpointing trainable deficits and managing the complexities of the return to sport transition. While the athlete returned to snow 7 months post-injury, presenting with interlimb asymmetries below 10%, functional and strength deficits persisted up to 18 months post-injury. More research is required to establish a valid return to sport protocol for alpine ski racers with ACL injury to safeguard against the high risk for ACL reinjury.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".