Injuries among professional ballet dancers in Finland: a prospective cohort study over five ballet seasons (FinBallet Study)
Bibliographic record
Abstract
OBJECTIVE: The aim of this prospective cohort study was to examine the incidence and characteristics of injuries in professional ballet dancers across multiple seasons. METHODS: Hundred-and-sixteen ballet dancers (median age 24; range 18-40, females 53%) from a professional ballet company agreed to participate and were followed for up to five ballet seasons. All dance-related injuries requiring a visit to a medical doctor were recorded by in-house physiotherapists. Injury rates per 100 dancer seasons were calculated and injury characteristics (i.e. anatomical location, tissue type, severity, and mechanism) were described. RESULTS: Hundred-and-sixty injuries occurred in 311 dancer-seasons, comprising an injury rate (IR) of 51.5 injuries (95% CI 45.9 to 57.0) per 100 dancer-seasons. Eighty-three percent of the injuries affected the lower limbs (IR 42.4, 95% CI 37.0 to 47.9). The ankle was the most injured body region (IR 15.4, 95% CI 11.4 to 19.5), followed by lower leg (IR 8.4, 95% CI 5.3 to 11.4), and knee (IR 6.1, 95% CI 3.5 to 8.8). Thirty-nine percent of injuries involved muscle/tendon structures, and 29% involved ligaments/joints. Fifty-seven percent of injuries were severe, causing more than 28 days absence from dance. Of all injuries, 57% were sudden onset, and 43% were gradual onset injuries. CONCLUSION: Results highlight the need for effective interventions to reduce the high incidence of lower limb injuries, including ankle sprains, tendon issues, muscle strains, and stress fractures in professional ballet dancers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".