Lower limb asymmetry in mechanical muscle function: A comparison between ski racers with and without <scp>ACL</scp> reconstruction
Bibliographic record
Abstract
Due to a high incidence of anterior cruciate ligament (ACL) re-injury in alpine ski racers, this study aims to assess functional asymmetry in the countermovement jump (CMJ), squat jump (SJ), and leg muscle mass in elite ski racers with and without anterior cruciate ligament reconstruction (ACL-R). Elite alpine skiers with ACL-R (n = 9; 26.2 ± 11.8 months post-op) and uninjured skiers (n = 9) participated in neuromuscular screening. Vertical ground reaction force during the CMJ and SJ was assessed using dual force plate methodology to obtain phase-specific bilateral asymmetry indices (AIs) for kinetic impulse (CMJ and SJ phase-specific kinetic impulse AI). Dual x-ray absorptiometry scanning was used to assess asymmetry in lower body muscle mass. Compared with controls, ACL-R skiers had increased AI in muscle mass (P < 0.001), kinetic impulse AI in the CMJ concentric phase (P < 0.05), and the final phase of the SJ (P < 0.05). Positive associations were observed between muscle mass and AI in the CMJ concentric phase (r = 0.57, P < 0.01) as well as in the late SJ phase (r = 0.66, P < 0.01). Future research is required to assess the role of the CMJ and SJ phase-specific kinetic impulse AI as a part of a multifaceted approach for improving outcome following ACL-R in elite ski racers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".