A Single Bout of On-Ice Training Leads to Increased Interlimb Asymmetry in Competitive Youth Hockey Athletes
Bibliographic record
Abstract
Interlimb asymmetry (ILA) refers to an anatomical or physiological imbalance between contralateral limbs, which can influence neuromuscular function. Investigating the influence of neuromuscular fatigue on ILA may be critical for optimizing training programs, injury rehabilitation, and sport-specific performance. The purpose of this study was to determine if a single bout of ice hockey-specific exercise creates or exacerbates lower-limb ILA. Before and after an on-ice training session, 33 youth ice-hockey athletes (14.9 [1.7] y; 11 females) performed 3 repetitions of a maximal vertical countermovement jump (CMJ), an eccentric hamstring contraction, and maximal isometric hip adduction and abduction contractions. Force- and power-related variables were analyzed to determine limb-specific neuromuscular function. The on-ice session reduced maximal isometric hip adduction (left: 7.3% [10.3%]; right: 9.5% [9.6%]) and abduction (left: 4.9% [6.9%]; right: 5.0% [8.1%]) force, but did not impair (P ≥ .10) CMJ performance (jump height, relative peak power, braking duration, and total duration). After the on-ice session, ILA was greater for CMJ propulsive impulse (6.3% [2.9%] vs 5.1% [2.6%]), CMJ braking rate of force development (19.3% [7.6%] vs 15.2% [6.4%]), and peak isometric hip adduction force (6.7% [5.5%] vs 6.1% [4.1%]). In conclusion, hockey-specific exercise leads to increased ILA for multiple force-related metrics, which may be a compensatory mechanism to maintain bilateral task performance when fatigued.
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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 | 0.000 | 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) | 0.001 | 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".