108 Protective equipment in youth ice hockey: are mouthguards and helmet age relevant in evaluating concussion risk?
Why this work is in the frame
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Bibliographic record
Abstract
<h3>Introduction</h3> 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. <h3>Materials and Methods</h3> 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. <h3>Results</h3> 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. <h3>Conclusions</h3> 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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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 | 0.003 | 0.002 |
| 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 | 0.001 |
| 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 it