Can pre-season fitness measures predict time to injury in varsity athletes?: a retrospective case control study
Bibliographic record
Abstract
BACKGROUND: The ability to determine athletic performance in varsity athletes using preseason measures has been established. The ability of pre-season performance measures and athlete's exposure to predict the incidence of injuries is unclear. Thus our purpose was to determine the ability of pre-season measures of athletic performance to predict time to injury in varsity athletes. METHODS: Male and female varsity athletes competing in basketball, volleyball and ice hockey participated in this study. The main outcome measures were injury prevalence, time to injury (based on calculated exposure) and pre-season fitness measures as predictors of time to injury. Fitness measures were Apley's range of motion, push-up, curl-ups, vertical jump, modified Illinois agility, and sit-and-reach. Cox regression models were used to identify which baseline fitness measures were predictors of time to injury. RESULTS: Seventy-six percent of the athletes reported 1 or more injuries. Mean times to initial injury were significantly different for females and males (40.6% and 66.1% of the total season (p < 0.05), respectively). A significant univariate correlation was observed between push-up performance and time to injury (Pearson's r = 0.332, p < 0.01). No preseason fitness measure impacted the hazard of injury. Regardless of sport, female athletes had significantly shorter time to injury than males (Hazard Ratio = 2.2, p < 0.01). Athletes playing volleyball had significantly shorter time to injury (Hazard Ratio = 4.2, p < 0.01) compared to those playing hockey or basketball. CONCLUSIONS: When accounting for exposure, gender, sport and fitness measures, prediction of time to injury was influenced most heavily by gender and sport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 teacher head, 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".