Understanding musculoskeletal disorders in dancers: The role of lumbopelvic muscles and movement competency
Bibliographic record
Abstract
OBJECTIVE: To investigate whether transversus abdominis activation (TrA), hip strength, and movement competency are associated with the incidence of musculoskeletal disorder episodes (MDEs) in dancers when controlling for confounding variables. The secondary objectives were to determine if there were differences between professional and preprofessional dancers for the aforementioned factors, as well as to determine if there were differences in TrA activation and hip strength between the dominant and non-dominant sides. DESIGN: Prospective cohort study. METHODS: 118 dancers were recruited. The independent variables were collected at the beginning of the dance season: 1) TrA activation, 2) hip strength, and 3) movement competency. To assess the development of MDEs, a weekly electronic diary was used over a 38-week period. MDEs were compiled for each dancer's whole body and subdivided into total musculoskeletal disorder episodes (all body parts) and lower quadrant musculoskeletal disorder episodes (lower limb and lower back). RESULTS: Lower TrA, as well as higher hip abductor and external rotator strength, were associated with a lower incidence of MDEs. TrA activation (β = 0.260, p = 0.023) and hip external rotator strength (β = -0.537, p = 0.002) could significantly explain 25.4% of the variance of total MDEs, as well as 20.9% of the variance of lower quadrant musculoskeletal disorder episodes (β = 0.272, p = 0.016; β = -0.459, p = 0.011). No significant associations were found between movement competency and MDEs. CONCLUSIONS: Higher hip strength could be a protective factor for MDEs among dancers. Further studies are needed to better understand the involvement of the transversus abdominis in MDEs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".