Musculoskeletal Injuries and Pain in Dancers a Systematic Review Update
Bibliographic record
Abstract
The objective of this study was to assemble and synthesize the best available literature from 2004 to 2008 on musculoskeletal injury and pain in dancers. MEDLINE and CINAHL were the primary sources of data. Indexed terms such as dance, dancer, dancing, athletic injuries, occupational injuries, sprains and strains, musculoskeletal diseases, bone density, menstruation disturbances, and eating disorders were used to search the databases. Citations were screened for relevance using a priori criteria, and relevant studies were critically reviewed for scientific merit by the best-evidence synthesis method. After screening, 19 articles were found to be scientifically admissible. Data from accepted studies were abstracted into evidence tables relating to: prevalence and associated factors; incidence and risk factors; intervention; and injury characteristics and prognosis of musculoskeletal injury and pain in dancers. Principal findings included: a high prevalence and incidence of lower extremity, hip and back injuries; preliminary evidence that psychosocial and psychological issues such as stress and coping strategies affect injury frequency and duration; history of a previous lateral ankle sprain is associated with an increased risk of ankle sprain in the contralateral ankle in dance students; fatigue may play a role in ACL injury in dancers; acute hamstring strains in dancers affect tendon more than muscle tissue, often resulting in prolonged absence from dance. It is concluded that, while there are positive developments in the literature on the epidemiology, diagnosis, prognosis, treatment, and prevention of MSK injuries and pain in dancers, much room for improvement remains. Suggestions for future research are offered.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.025 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".