11.28 What is the relationship between mouthguard use and concussion incidence in professional male rugby union?
Bibliographic record
Abstract
Objective To investigate the association between mouthguard use and match concussion incidence. Design Case-control (cases: match concussions, controls: non-concussion contact injuries). Setting All participating teams in the top tier of professional rugby union in England, 2013–2019. Participants 1436 male professional rugby union players. Interventions (or Assessment of Risk Factors) Medical staff reported mouthguard use at the time of injury/concussion. Outcome Measures Odds of sustaining a match concussion when wearing a mouthguard or not. Main Results Match concussion incidence (2013–19) was 17 per 1000 hours (95% CI:14–20). Sixty-five percent of cases and 54% of controls wore mouthguards. Mouthguard use was associated with concussion injury (adjusted odds ratio: 1.51, 95% CI:1.25–1.82). Median number of days absent due to injury was equal for both cases and controls (9 days). Sustaining a concussion in the current or previous season was associated with a nearly three-fold increased odds of concussion (odds ratio: 2.98, 95% CI:2.58–3.44). Age and position had no significant effect on concussion risk (OR: 0.99, 95% CI:0.97–1.01 and OR: 1.05, 95% CI:0.88–1.27, respectively). Conclusions The use of mouthguards in this professional rugby setting was associated with increased odds of concussion. This was an unexpected finding and the reasons for it are unclear. Mouthguards protect against dental injuries and should still be recommended, but further evidence is needed to understand the relationship with concussion, in particular regarding mouthguard use in non-injured populations, risk taking behaviours of concussed vs non-concussed athletes and mouthguard type (boil and bite/custom fit).
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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 | 0.011 |
| 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) | 0.010 | 0.001 |
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".