Clarifying the relationships between trunk muscle endurance, respiratory muscle strength and static/dynamic postural control in Latin dancers
Bibliographic record
Abstract
Sufficient study has not been performed to clarify the role of trunk/respiratory muscles strength/endurance in providing postural control in dancers. The purpose of this study was to identify predictors affecting static/dynamic postural control in Latin dancers and to compare these measurements with non-dancers. The study included 38 (26F; 12 M) Latin dancers and 33 (21F; 11 M) non-dancers. Static/dynamic postural control, trunk muscle endurance, respiratory muscle strength/pulmonary functions, flexibility, and reaction tests were assessed with a force platform system, the McGill battery, spirometer, sit-and-reach test and Nelson-reaction-tests, respectively. Trunk muscle endurance times, respiratory muscle strength, FEV1/FVC, gender, hours of training per week and dancing experience were significant predictors of static/dynamic postural control in dancers (p < 0.05). All the trunk muscle endurance times, reaction tests results, and maximal inspiratory pressure were higher in the dancers compared to the non-dancers (p < 0.05). The limits of stability for forward and backward directions were higher, and anteroposterior sway in normal stability with eyes open was lower in the dancers compared to the non-dancers (p < 0.05). Trunk muscles endurance, respiratory muscle strength, dancing experience, and hours of training per week were positively associated with static/dynamic postural control. These predictors should be taken into consideration to improve postural control in dancers.
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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".