Concussion rates and recovery in elite youth ice hockey players
Bibliographic record
Abstract
Objective To examine rates of concussion and severe concussion (time loss greater than 10 days) in elite youth ice hockey players. Design Prospective cohort study Setting Alberta, Canada Participants Bantam (13–14 years) and Midget (15–17 years) male and female elite (top 30% by division of play) youth ice hockey players. Assessment of risk factors Participants 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). Outcome measures 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. Main results Overall, 778 elite youth ice hockey players (659 males, 119 females; aged 13–17 years) participated in this study. In total, 143 concussions occurred. The concussion incidence proportion (IP) was 18 concussions/100 players. The concussion incidence rate (IR) was 1.31 concussions/1000 player-hours (95% CI; 1.09, 1.57). Time loss 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. Competing interests None.
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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".