Concussion in Youth Hockey: Prevalence, Risk Factors, and Management across Observation Strategies
Bibliographic record
Abstract
Abstract Ice hockey ranks among the highest of all sports for rates of concussion, and estimates from youth hockey appear ominously close to estimates from the NHL (23.15 and 29.59 per 1000 player-hours, respectively), yet concussion is seldom studied in the youth setting, particularly in a way that accounts for under-reporting. To maximize the capture of concussions in youth hockey, we used broad injury inclusion criteria and multiple surveillance strategies, including (a) official injury reports, (b) reports from team personnel, and (c) reports from trained hockey observers. The aims were to (a) better elucidate the prevalence and causes of hockey-related concussions, (b) examine how concussions are reportedly managed in youth ice hockey, and (c) speak to the utility of the different surveillance strategies. Contact between players was the most common mechanism across observation strategies and more than half (51 %) of concussions reported by volunteers were caused by illegal acts (32 % hits from behind, 8 % hits to head, and 7 % crosschecks), though few (23 %) resulted in penalties. According to volunteer and observer reports, many young players are returning to play in the same game they sustained a concussion (34 % and 71 %, respectively), which contravenes Hockey Canada guidelines. Contrary to the literature, there were significantly higher odds (p<0.05) of sustaining a concussion in the youngest age division rather than among older players according to volunteer reports. This study sampled approximately 22 400 youth players and is among the broadest investigations of concussion in youth ice hockey.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".