Injury Patterns and Injury Rates in the Circus Arts
Bibliographic record
Abstract
BACKGROUND: Human circus arts are gaining increasing popularity as a physical activity with more than 500 companies and 200 schools. The only injury data that currently exist are a few case reports and 1 survey. HYPOTHESIS: To describe injury patterns and injury rates among Cirque du Soleil artists between 2002 and 2006. STUDY DESIGN: Descriptive epidemiology study. METHODS: The authors defined an injury as any work-related condition recorded in an electronic injury database that required a visit to the show therapist. Analyses for treatments, missed performances, and injury rates (per 1000 artist performances) were based on a subset of data that contained appropriate denominator (exposure) information (began in 2004). RESULTS: There were 1376 artists who sustained a total of the 18 336 show- or training-related injuries. The pattern of injuries was generally similar across sex and performance versus training. Most injuries were minor. Of the 6701 injuries with exposure data, 80% required < or =7 treatments and resulted in < or =1 completely missed performance. The overall show injury rate was 9.7 (95% confidence interval, 9.4-10.0; for context, published National Collegiate Athletic Association women's gymnastics rate was 15.2 injuries per 1000 athlete-exposures). The rate for injuries resulting in more than 15 missed performances for acrobats (highest risk group) was 0.74 (95% confidence interval, 0.65-0.83), which is much lower than the corresponding estimated National Collegiate Athletic Association women's gymnastics rate. CONCLUSION: Most injuries in circus performers are minor, and rates of more serious injuries are lower than for many National Collegiate Athletic Association sports.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".