The effect of rest days on injury rates
Bibliographic record
Abstract
Despite the importance of recuperation, few have studied the impact of rest periods on injury prevention. We determined the effect of rest days (breaks) on injury rates and treatments using electronic injury records from an acrobatic circus company that employs former world-class athletes as acrobats. To account for accumulated fatigue, we considered breaks across SD3 (third consecutive week of 1-day rest) to SD6 as a single exposure level (SD3-6), and vacation and DD (2-day rest) as a single exposure level. Medical attention injury rates were increased post- vs pre-break {rate ratio 1.45 [95% confidence intervals (95% CI): 1.22-1.73]} with less of an effect for 1-day time loss [1.25 (95% CI: 0.58-2.67)] and 15-day time loss [1.10 (95% CI: 0.26-4.56)]. However, the increase in injury rate post break for SD3-6 was similar to that of DD-Vacation (P=0.48, 0.53, and 0.65) for medical attention, and both ≥1 day and ≥15 days time loss, respectively. The increase in the number of treatments post-break was less for SD3-6 vs DD-vacation. Our findings suggest that 2-day breaks every four to 6 weeks may be sufficient to avoid an increasing injury rate due to cumulative fatigue in professional acrobatic 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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".