Examining the effect of the injury definition on risk factor analysis in circus artists
Bibliographic record
Abstract
A secondary data analysis of a prospective cohort study was conducted to explore how different definitions of injury affect the results of risk factor analyses. Modern circus artists (n=1281) were followed for 828,547 performances over a period of 49 months (2004-2008). A univariate risk factor analysis (age, sex, nationality, artist role) estimating incidence rate ratios (IRR) with 95% confidence intervals (95% CI) was conducted using three injury definitions: (1) medical attention injuries, (2) time-loss injuries resulting in ≥1 missed performances (TL-1) and (3) time-loss injuries resulting in >15 missed performances (TL-15). Results of the risk factor analysis were dependent on the injury definition. Sex (females to male; IRR=1.13, 95% CI; 1.02-1.25) and age over 30 (<20 years to >30 years; IRR=1.37, 95% CI; 1.07-1.79) were risk factors for medical attention injuries only. Risk of injury for Europeans compared with North Americans was higher for TL-1 and TL-15 injuries compared with medical attention injuries. Finally, non-sudden load artists (low-impact acts) were less likely than sudden load artists (high-impact acts) to have TL-1 injuries, but the risk of medical attention injuries was similar. The choice of injury definition can have effects on the magnitude and direction of risk factor analyses.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".