Reliability of assessing ballet dancers’ postural stability in the unshod and the en pointe relevé position with a smartphone application
Bibliographic record
Abstract
Purpose This study aimed to verify the reliability of estimating ballet dancers’ postural stability during the unshod and the en pointe relevé position with a smartphone application Methods The participants (13 ballet dancers, 22.4 ± 2.5 years of age) were tested in the unshod and the en pointe relevé position (YMED Balance Test application, smartphone secured at the L5 level for centre of mass approximation, 10 trials for each condition, 10 seconds per trial, 2-minute intertrial break, arms relaxed at bodyside, gaze fixated at an eye-level target, preferred feet width and orientation). Paired t-tests examined the inter-condition differences. Relative (intraclass correlation coefficient, ICC) and absolute (standard error of measurement, SEM, SEM%) reliability indices (for accumulated and paired trials) were computed for each condition (SPSS software v. 26.0, p < 0.05). Results The total balance score and all centre of mass spatial measures indicated worse postural stability in the en pointe condition (p < 0.05), with no significant temporal differences (p > 0.05). The total body balance score was the most reliable measure (good to excellent ICC s, low to moderate SEM%) with a minimum of 8 trials ensuring reliability in both the unshod and the en pointe relevé positions. Conclusions Taken a minimum of 8 trials and the measure of total balance score, we may obtain a reliable estimation of ballet dancers’ postural stability in the unshod and the en pointe relevé position by using the YMED Balance Test smartphone application.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".