Concussion Burden, Recovery, and Risk Factors in Elite Youth Ice Hockey Players
Bibliographic record
Abstract
OBJECTIVE: To examine rates of concussion and more severe concussion (time loss of greater than 10 days) in elite 13- to 17-year-old ice hockey players. METHODS: This is a prospective cohort study (Alberta, Canada). Bantam (13-14 years) and Midget (15-17 years) male and female elite (top 20% by division of play) youth ice hockey players participated in this study. Players completed a demographic and medical history questionnaire and clinical test battery at the beginning of the season. A previously validated injury surveillance system was used to document exposure hours and injury during one season of play (8 months). Players with a suspected ice hockey-related concussion were referred to the study sport medicine physicians for assessment. Time loss from hockey participation was documented on an injury report form. RESULTS: Overall, 778 elite youth ice hockey players (659 males and 119 females; aged 13-17 years) participated in this study. In total, 143 concussions were reported. The concussion incidence rate (IR) was 17.60 concussions/100 players (95% CI, 15.09-20.44). The concussion IR was 1.31 concussions/1000 player-hours (95% CI, 1.09-1.57). Time loss of greater than 10 days was reported in 74% of cases (106/143), and 20% (n = 28) had time loss of greater than 30 days. CONCLUSIONS: Concussion is a common injury in elite youth ice hockey players. In this study population, a large proportion of concussions (74%) resulted in a time loss of greater than 10 days, possibly reflecting more conservative management or longer recovery in youth athletes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".