Does a Bimolar Mouthguard Alter Ventilation of Hockey Players?
Bibliographic record
Abstract
Athletes competing in contact sports commonly wear intra-oral dental mouthguards. Data are sparse concerning the influence of a mouthguard on breathing during exercise. We compared VE and VO2 during submaximal and maximal exercise on a skating treadmill (TM) while wearing an intra-oral dental mouthguard. Female varsity hockey players (n = 12) performed two skating tests on a TM with and without a mouthguard (WIPSS Jaw-Joint Protector). The players wore the mouthguard during hockey practices prior to collection of ventilation data on the treadmill. Also, the players completed a questionnaire that examined their perception of the mouthguard in terms of ventilation, comfort and performance. A 10-point rating scale was used for this evaluation. Two performance tests on the skating treadmill examined the effect of the mouthguard on submaximal and maximal aerobic exercise. The subjects skated for 4 min at 2 submaximal velocities (14 and 16 km h-1), separated by 5 min of passive recovery. A VO2max test followed the submaximal tests and commenced at 18 km h-1 with the velocity increasing by 1 km h-1 every minute until volitional fatigue. VE, VO2, VCO2 and RER were analyzed using a Sensor Medics 2900 metabolic cart. Ventilation was unchanged when skating at the two submaximal velocities. VC2max was 48.8 ml/kg.min using the intra-oral mouthguard and was 52.4 ml/kg.min without a mouthguard. VEmax was 108.5 L/min using the intra-oral mouthguard and was 114.1L/min without a mouthguard. The results showed that VEmax and VO2max were lower using the mouthguard compared to the no-mouthguard condition.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".