Effects of Neuromuscular Exercise on Dynamic Balance, Vertical Jump and Trunk Endurance in Ice Hockey Players: A Randomized Controlled Trial
Bibliographic record
Abstract
This study aims to investigate the impact of a 12-week neuromuscular exercise program on balance, vertical jump, and core endurance parameters in female ice hockey players. Y balance test, vertical jump test and McGill test were used for evaluation of dynamic balance, vertical jump and trunk endurance, respectively. A 12-week training program was conducted on 50 female ice hockey players. The exercise group showed significant differences in anterior and posteromedial balance scores for both right and left (p<0.05), but there was no significant difference in posterolateral scores (p>0.05). The control group didn't show significant improvements in the vertical jump (p>0.05), while the neuromuscular exercise group demonstrated a statistically significant improvement (p<0.05). Trunk extension endurance improved significantly in the control group (p<0.05), but there were no significant differences in trunk flexion and lateral endurance (p>0.05). In contrast, the neuromuscular exercise group significantly improved all trunk endurance values (p<0.05). Neuromuscular exercise training applied to ice hockey players can improve the balance, vertical jump, and trunk endurance parameters. Therefore, the inclusion of neuromuscular exercise programs in the training programs of female ice hockey players can enhance their physical performance and may reduce the risk of injury.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".