Mouthguard use in youth ice hockey and the risk of concussion: nested case–control study of 315 cases
Bibliographic record
Abstract
BACKGROUND: Concussion is the most common injury in youth ice hockey. Whether mouthguard use lowers the odds of concussion remains an unanswered question. OBJECTIVE: To determine the association between concussion and mouthguard use in youth ice hockey. METHODS: Nested case-control design. Cases and controls were identified from two prospective cohort studies using valid injury surveillance methods. Cases were players concussed during a game or practice; controls were players who sustained a non-concussion injury during a game or practice. The primary exposure was mouthguard use at time of injury; mouthguard type (dental custom fit or off the shelf) was a secondary exposure. Physician-diagnosed or therapist-suspected concussion was the primary outcome. Dental injury was a secondary outcome. Multilevel logistic regression with random effect at a team level was used to obtain ORs for the mouthguard effect, adjusted for level of play, age group, position, concussion history, mechanism of injury, cohort, session type and body checking policy. RESULTS: Among cases, 236/315 (75%) were wearing a mouthguard at time of injury, while 224/270 (83%) controls were wearing a mouthguard at time of injury. Any mouthguard use was associated with an adjusted OR for concussion of 0.36 (95% CI 0.17 to 0.73). Off-the-shelf mouthguards were associated with a 69% lower odds of concussion (adjusted OR: 0.31; 95% CI 0.14 to 0.65). Dental custom-fit mouthguards were associated with a non-significant 49% lower odds of concussion (adjusted OR: 0.51; 95% CI 0.22 to 1.10). No dental injuries were identified in either cohort. CONCLUSION: Mouthguard use was associated with lower odds of concussion. Players should be required to wear mouthguards in youth ice hockey.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".