Hip adductor muscle function in forward skating
Bibliographic record
Abstract
Adductor strain injuries are prevalent in ice hockey. It has long been speculated that adductor muscular strains may be caused by repeated eccentric contractions which decelerate the leg during a stride. The purpose of this study was to investigate the relationship of skating speed with muscle activity and lower limb kinematics, with a particular focus on the role of the hip adductors. Seven collegiate ice hockey players consented to participate. Surface electromyography (EMG) and kinematics of the lower extremities were measured at three skating velocities 3.33 m/s (slow), 5.00 m/s (medium) and 6.66 m/s (fast). The adductor magnus muscle exhibited disproportionately larger increases in peak muscle activation and significantly prolonged activation with increased speed. Stride rate and stride length also increased significantly with skating velocity, in contrast, hip, knee and ankle total ranges of motion did not. To accommodate for the increased stride rate with higher skating speeds, the rate of hip abduction increased significantly in concert with activations of adductor magnus indicating a substantial eccentric contraction. In conclusion, these findings highlight the functional importance of the adductor muscle group and hip abduction-adduction in skating performance as well as indirectly support the notion that groin strain injury potential increases with skating speed.
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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 | 0.001 |
| 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) | 0.002 | 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".