THE ROLE OF FUNCTIONAL DYNAMIC STRETCHING TRAINING IN DANCE SPORTS
Bibliographic record
Abstract
ABSTRACT Introduction Dynamic stretching is a particular form of training. Currently, there is little research in academia about dynamic stretching in sports dancing. Objective Explore the role of functional dynamic stretching training in dance sports. Methods 60 sports dancers with a history of ankle injuries were randomly divided into a control and experimental group. All performed a training protocol twice a week, lasting 45 minutes, for eight weeks. A functional dynamic stretching training session was added to the control group. The effects were evaluated by the Cumberland scale, bilateral stability comparison, and balance control by the Perkin system. Data were statistically treated for analysis. Results There was no significant difference between the scores of healthy ankle joints and injured ankle joints in the two groups (P>0.05). After eight weeks of functional dynamic stretching training, there was a significant difference between the experimental and control groups on injured ankle joints (P<0.05). Conclusion Dynamic stretching training can effectively improve ankle joint stability in sports dancers. Concomitantly, this method effectively prevents injuries to the athlete’s ankle joint. Evidence level II; Therapeutic Studies - Investigating the results.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.050 | 0.005 |
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".