Untangling risk factors including discipline-specific exposure for injuries in preprofessional and professional circus artists in the USA
Bibliographic record
Abstract
Objective This prospective cohort study quantified injury patterns related to specific circus discipline exposure in preprofessional and professional circus artists. Methods Circus artists (n=201; ages 13–69; 172 female, 29 male assigned sex at birth) were enrolled in 10 cities across the USA. Participants were followed for 1 year from enrolment, completing a weekly training log and undergoing a physical therapist evaluation for injuries. The circus-specific extension of the International Olympic Committee 2020 consensus on recording injury and illness in sports was used to analyse injury patterns. Results The study completion rate was 77% (n=155). Data were analysed by participant subgroup (age, professional status, sex at birth). The highest injury rates in participant subgroups were for males (5.69/1000 exposures) and related to discipline subgroups, were in aerial with ground elements (5.93/1000 exposures) and aerial (4.26/1000 exposures). Adults had more injuries related to aerial, whereas adolescents had more related to ground disciplines (χ 2 (2)=10.62, p=0.005) and non-time loss injuries (χ 2 (1)=5.45, p=0.02). Females had a higher proportion of repetitive injuries (70% vs 55%) than males (χ 2 (1) = 4.43, p=0.035). Individuals with an eating disorder history had more (p<0.004) injuries (mean 2.27±2.29) than those without (mean=1.48±0.96). Conclusions This study showed that intrinsic factors (age, sex at birth and history of eating disorder) and extrinsic factors (circus discipline exposure) affect injury risk. We need to account for the intersectionality of these factors to address risk management at an individual and group level.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".