Pre-Season Screening Assessments: Normative Data for Pre-Professional Ballet Dancers
Bibliographic record
Abstract
OBJECTIVES: Pre-professional dance is high-risk, with injury incidence up to 4.7 injuries/1000 dance hours. Pre-season screening measures have been utilized to assess risk factors for dance-related injury, however normative values haven't been established for a pre-professional ballet population. The purpose of this study was to establish normative values of ankle and hip joint range of motion (ROM), lumbopelvic control, and dynamic balance pre-season screening measures for pre-professional ballet dancers. METHODS: 498 adolescent pre-professional ballet dancers [n = 219 junior division (194 female, 25 male; mean age: 12.9±0.9 year); n = 281 senior division (238 female, 41 male; mean age: 16.8±1.5 year)] participated in baseline screening tests across 5 seasons (2015-2019). Baseline measures took place at the beginning of each academic year: ankle ROM [dorsiflexion (deg); plantarflexion (PF) (deg)], total active turnout (TAT) (deg), lumbopelvic control [active straight leg raise (ASLR) (score); one leg standing test (OLS) (score)], and dynamic balance [unipedal balance (sec); Y-Balance Test (cm)]. RESULTS: Percentiles for ankle dorsiflexion ranged from 28.2° (male senior division, 10th percentile) to 63.3° (female junior division, 100th percentile). For PF, percentiles ranged from 77.5 to 111.8° (male junior division, 10th percentile; male senior division, 100th percentile). Percentiles for TAT for all participants ranged between 121.1° and 131.0°. For the ASLR, the proportion of participants moving with compensation (pelvis shifting) was between 64.0% and 82.2%. For OLS, 19.7% to 56.1% of dancers had a positive score (hip hiking). Percentiles for dynamic balance ranged from 3.5 to 17.1 seconds (unipedal dynamic balance) and 75.8 to 103.3 cm (YBT composite reach score) across all groups. CONCLUSION: The establishment of normative values of pre-season screening measures among a pre-professional ballet population can be used to determine areas to target during training, recognize individuals with possible injury risk, and inform return to dance protocols following injury. Comparison with other dancer/athletic populations will also provide insight into the performance of dancers and identify areas in need of improvement.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".