Circus-specific extension of the International Olympic Committee 2020 consensus statement: methods for recording and reporting of epidemiological data on injury and illness in sport
Bibliographic record
Abstract
Indepth knowledge of injury and illness epidemiology in circus arts is lacking. Comparing results across studies is difficult due to inconsistent methods and definitions. In 2020, the International Olympic Committee (IOC) consensus group proposed a standard method for recording and reporting epidemiological data on injuries and illnesses in sports and stated that sport-specific extension statements are needed to capture the context of each sport. This is the circus-specific extension to be used with the IOC consensus statement. International circus arts researchers in injury and illness epidemiology and performing arts medicine formed a consensus working group. Consensus statement development included a review of literature, creation of an initial draft by the working group, feedback from external reviewers, integration of feedback into the second draft and a consensus on the final document. This consensus statement contains circus-specific information on (1) injury definitions and characteristics; (2) measures of severity and exposure, with recommendations for calculating the incidence and prevalence; (3) a healthcare practitioner report form ; (4) a self-report form capturing health complaints with training and performance exposure ; and (5) a demographic , health history and circus experience intake questionnaire . This guideline facilitates comparing results across studies and enables combining data sets on injuries in circus arts. This guideline informs circus-specific injury prevention, rehabilitation, and risk management to improve the performance and health of circus artists.
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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 | 0.253 | 0.470 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.014 | 0.013 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".