Implementation of Injury and Illness Surveillance Protocols in Varsity Athletes
Bibliographic record
Abstract
OBJECTIVE: To develop and implement a prospective varsity athlete surveillance system to identify injury and illness trends in a multisport varsity-level university setting. DESIGN: Longitudinal prospective surveillance study. SETTING: Varsity-level sport program at the University of Guelph, Guelph, ON, Canada. PARTICIPANTS: Athletic therapists (ATs) (n = 35) from 17 varsity sports provided injury and illness information on 624 varsity-level athletes (381 men and 243 women) during the 2016/2017 competitive season. INTERVENTIONS: Team ATs reported athlete health complaints weekly. Athletes reported additional details on the injury or illness that was reported. MAIN OUTCOME MEASURES: The outcome measurements included when the injury or illness occurred, anatomical location, diagnosis, cause, perceived severity, treatment, estimated athlete exposure (AE) to training and competition, and time loss from sport. Measures were recorded from the teams' first competition through to the end the season and/or playoffs. RESULTS: Twenty-nine of 30 varsity sports teams participated in this injury and illness surveillance protocol. The compliance of team ATs and varsity athletes was 89% (men: 94%, women: 82%). The overall injury and illness rates were 5.5 injuries and 1.7 illnesses per 1000 AEs, respectively. Men's injury rates were greater than women's, and injury rates of contact sports were greater than noncontact sports. CONCLUSIONS: The utilization of a prospective injury and illness surveillance protocol in the Canadian University sport system is feasible with good athlete, AT, and coach acceptance. The surveillance data should inform future injury and illness prevention strategies.
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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 | 0.092 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".