Effect of Training on Postural Control in Figure Skaters
Bibliographic record
Abstract
OBJECTIVES: To compare the effect of a neuromuscular training program and a basic exercise program on postural control in figure skaters. DESIGN: Two groups; parallel design; prospective, randomized controlled trial. SETTING: Postural control laboratory, arenas, September 2001 to December 2002. PARTICIPANTS: Forty-four young, healthy figure skaters (18 years +/- 3 years). INTERVENTIONS: Participants were randomly assigned to receive a neuromuscular training program (n = 22) or a basic exercise training program (n = 22). Both programs were completed 3 times per week for 4 weeks, and each session was supervised. MAIN OUTCOME MEASUREMENTS: Participants completed baseline and postintervention measures of postural control on a force plate. Postural control was quantified as the center of pressure (CoP) path length during tests of single-limb standing balance that mimicked figure skating skills and challenged the postural control system to varying degrees. The primary outcome measure was the CoP path length observed during a landing jump test completed with eyes closed. RESULTS: The post intervention CoP path lengths during the more challenging tests were significantly (P < 0.05) lower (indicating better postural control) for the neuromuscular trained group than for the basic exercise-trained group. For the landing jump test completed with eyes closed, the percent improvement in the neuromuscular trained group was significantly greater (mean = 21.0 +/- 22.0%) than the basic exercise trained group (mean = -4.9 +/- 24.9%; P < 0.05). The magnitude of improvement in the neuromuscular-trained group ranged from approximately 1% to 21%, depending on the specific postural control test used. CONCLUSIONS: The results suggest that off-ice neuromuscular training can significantly improve postural control in figure skaters, whereas basic exercise training does not.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".