Injury Prevalence Among Elite Adolescent Ballet Dancers is not Affected by Frequency of Questionnaire Administration: A 6-Month Exploratory Prospective Study
Bibliographic record
Abstract
Introduction: The Oslo Sports Trauma Research Centre Questionnaire on Health Problems (OSTRC-H) is a valid and reliable registration method for self-reported injuries and is regularly used among dancer populations. Monthly questionnaire administration is acceptable in athlete populations but has not been evaluated in dancers. The aim of this exploratory study was to assess the influence of weekly versus monthly administration of the OSTRC-H on estimated injury outcomes among elite adolescent ballet dancers. Methods: Participants (n = 103) were prospectively followed for 6 months and completed the OSTRC-H online, evaluating perceived consequences of self-reported health problems during the previous week and the previous 4 weeks, respectively. Four definitions of dance-related injury were utilized: (1) all complaints, (2) substantial, (3) medical attention, and (4) time-loss injuries. Descriptive statistics estimated: (1) the number of injuries reported (count), (2) average injury prevalence [proportion, 95% confidence intervals (CI)], (3) average severity score (0-100), and (4) days of time loss (count) for each injury definition. The 4 outcome measures were then compared between weekly and monthly registration with paired sample t-tests ( P < .05) and overlapping 95% CI. Results: A significant difference between the number of all complaints injuries (weekly: 133; monthly: 94; P < .001) and substantial injuries (weekly: 64; monthly: 45; P = .012) was found. Regardless of injury definition, there were no significant differences between injury prevalence, severity scores, and days of time loss when reported weekly versus monthly. Conclusion: Monthly administration of the OSTRC-H is an acceptable method to estimate injury prevalence, severity scores, and days of time loss amongst elite adolescent ballet 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".