Do Oral Mouthguards Affect Ventilation?
Bibliographic record
Abstract
Athletes participating in contact sports wear mouthguards (MG) to decrease the risk of concussions and orofacial injuries. However, many athletes resist wearing mouthguards citing reasons that include discomfort, problems with speech and breathing during play. Breathing difficulties may suggest some limitations with ventilation. PURPOSE To examine peak inspiratory and peak expiratory air flow at specific ventilatory rates using different types of MG and a no MG condition. METHODS Seven MG (3 stock, 3 boil and bite, and 1 custom-fitted; 5 unimolar and 2 bimolar) and a no MG condition were compared. MG were fitted into an oral dental model and air was ventilated through the model at three flow rates (30, 45, 60 strokes·min−1) using 2 and 3 liter syringes. Inspiratory and expiratory flows were recorded using a Medisoft Ergocard. Peak flow (L·s−1), FEF25, FEF50, FEF75, FIF25, FIF50 and FIF75 were recorded for 10 strokes at each ventilation rate. Data were analyzed using a 3-way (8 MG; 6 flow rates; 2 flow directions Insp/Exp) repeated measures ANOVA for the dependant variable, peak flow. RESULTS There was a significant main effect for MG (F = 11.97; p < 0.001) with lower peak air flow for the two bimolar MG compared to the no MG condition. There were significant interaction effects between MG and flow rates. At the lowest ventilation (60 L·min−1), peak flow was similar to the no MG condition for 5 of the 7 MG. At the highest ventilation (180 L·min−1), peak flow was significantly higher with no MG compared to 4 of the 7 MG conditions, including both bimolar MG. CONCLUSIONS These findings suggest that mouthguards do not impair ventilation at low flow rates, however peak flow is lowered at high ventilation with bimolar mouthguards and some unimolar mouthguards.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".