108 Protective equipment in youth ice hockey: are mouthguards and helmet age relevant in evaluating concussion risk?
Bibliographic record
Abstract
Introduction The high concussion burden in youth ice hockey is concerning. An important yet understudied area for prevention is protective equipment (e.g., wearing a mouthguard, age of helmet). Therefore, the objective of this study was to compare incidence rates of concussion between players based on mouthguard use and helmet age. Materials and Methods This prospective cohort collected concussion information and player participation over five seasons (2013/14–2017/18) in male and female youth ice hockey players (ages 11–18). Baseline assessments were completed near the season start and collected reports on mouthguard use (yes, no), helmet age (newer/<2 years old, older/≥2 years old), and other important covariables (i.e., weight, age group, position of play, concussion history, body checking). Moreover, each player’s participation hours and the number of therapist-suspected and physician-diagnosed concussions were collected throughout each season. A multilevel negative binomial regression model was used to estimate the concussion incidence rate and incidence rate ratio (IRR) for equipment. Results The model included 426 player concussions (suffered by 369 players) with 271,148.7 player-hours and was adjusted for covariables, clustered by team, and offset by player-hours. Results showed that players who reported wearing a mouthguard had a 28% lower concussion rate compared with non-wearers (IRR=0.72, 95%CI: 0.55–0.93) while no differences in the concussion rate between newer and older helmet ages (IRR=0.94, 95%CI: 0.76–1.16) were detected. Conclusions Wearing a mouthguard was associated with significantly lower concussion rates; thus, policy mandating use should be considered in youth ice hockey. More specific helmet age categories may require further investigation.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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 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".