Protective equipment in youth ice hockey: are mouthguards and helmet age relevant to concussion risk?concussionfor mouthguard wearers
Bibliographic record
Abstract
OBJECTIVES: To compare the incidence rates and odds of concussion between youth ice hockey players based on mouthguard use and helmet age. MATERIALS AND METHODS: Within a 5-year longitudinal cohort (2013/2014 to 2017/2018) of male and female ice hockey players (ages 11-18; n=3330 players) in Alberta (Canada), we analysed the relationship of equipment and concussion in both a prospective cohort and nested case (concussion) control (acute musculoskeletal injury) approach. The prospective cohort included baseline assessments documenting reported mouthguard use (yes/sometimes, no use), helmet age (newer/<2 years old, older/≥2 years old) and important covariables (weight, level of play, position of play, concussion history, body checking policy), with weekly player participation throughout the season. The nested case-control component used injury reports to document equipment (mouthguard use, helmet age) and other information (eg, mechanism and type of injury) for the injury event. Multivariable mixed effects negative binomial regression (prospective cohort, incidence rate ratios (IRRs)) and multivariable mixed effects logistic regression (nested case-control, odds ratios (OR)) examined the association between equipment and concussion. RESULTS: Players who reported wearing a mouthguard had a 28% lower concussion rate (IRR=0.72, 95% CI 0.56 to 0.93) and 57% lower odds of concussion (OR=0.43, 95% CI 0.27 to 0.70) compared with non-wearers. There were no associations in the concussion rate (IRR=0.94, 95% CI 0.75 to 1.15) and odds (OR=1.16, 95% CI 0.73 to 1.86) between newer and older helmets. CONCLUSIONS: Wearing a mouthguard was associated with a lower concussion rate and odds. Policy mandating use should be considered in youth ice hockey. More research is needed to identify other helmet characteristics (eg, quality, fit) that could lower concussion risk.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".