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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".