Does the Movement Competency Screen Correlate with Deep Abdominals Activation and Hip Strength for Professional and Pre-professional Dancers?
Bibliographic record
Abstract
BACKGROUND: Dancers are a unique category of athletes who are frequently injured and experience pain. The primary cause of dance injuries is overuse, which could potentially be prevented. However, literature is scarce regarding validated methods of evaluating the risk of injury in dancers. The Movement Competency Screen (MCS) could potentially fill this gap. HYPOTHESIS/PURPOSE: To investigate the validity of the Movement Competency Screen (MCS) for dancers by 1) examining the correlation between scores on this functional test and the activation of deep abdominals and hip strength; 2) investigating the correlation between MCS scores and those of the Functional Movement Screen (FMS™). STUDY DESIGN: Cross-sectional study. METHODS: A total of 77 pre-professional and professional dancers from ballet and contemporary backgrounds were evaluated. The activation of deep abdominals was evaluated using ultrasound imaging and the hip strength was evaluated using a handheld dynamometer. The FMS™, another tool evaluating fundamental movement competency, was also administered. RESULTS: The dancers' MCS score was correlated with the activation of the transversus abdominis (r=0.239, p=0.036) and the strength of hip abductors (r=0.293, p=0.010), adductors (r=0.267, p=0.019) and external rotators (r=0.249, p=0.029). The MCS score was also correlated with the FMS™ score (r=0.489, p<0.001). CONCLUSION: This study shows that the MCS score is correlated with deep abdominal activation and hip strength in dancers, as well as with the FMS™ score. These findings provide evidence toward the validation of the MCS in dancers. LEVELS OF EVIDENCE: Level 2B.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".