L’hypermobilité articulaire, quantifiée par le score de Beighton, est-elle un facteur de risque de blessures musculosquelettiques chez les danseurs professionnels et préprofessionnels ?
Bibliographic record
Abstract
Background: Dance is a physical and artistic activity in which aesthetics and elegance are based on movements exceeding the physiological range of motion. Joint hypermobility, characterized by the ability of some joints to move through extreme range of motion, is commonly observed among dancers. Although it is highly sought after by dancers who want to reach the highest level, the physical constraints are not to be underestimated. Objective : The purpose of this review is to study whether joint hypermobility, quantified by the Beighton score, is a risk factor for musculoskeletal injuries in professional and preprofessional dancers. Method : The literature search was conducted on three electronic scientific databases : PubMed, ScienceDirect, and Cochrane Library. Selection, data extraction, and risk of bias assessment (using the Newcastle-Ottawa Scale) were performed by a single reviewer. Based on previously defined eligibility criteria, 17 articles were selected, and 4 were finally included in the review after a full reading. Results : The results showed high heterogeneity and limited comparability. Only two of the four studies analyzed reported statistically significant results, with p < 0,05 and RR/OR > 1. Discussion : However, the results should be interpreted with caution due to the limited number of studies, the small sample sizes, mostly composed of female dancers, and the presence of multiple sources of bias. Moreover, a high prevalence of injuries was noted, suggesting that further research should be conducted to obtain solid and reliable conclusions supported by high levels of evidence.
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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".