The Physiological Responses Of A Mandibular Repositioning Mouthguard And Their Effects On Athletic Performance
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to understand the physiological responses of a mandibular repositioning mouthguard on athletic performance. We hypothesize that MG designs that produce anatomically desirable jaw adjustments for an increase in the upper airway may have the ability to elicit ergogenic effects thereby improving respiratory capacity and athletic performance. METHODS: Twenty-four active participants volunteered for this study and were randomly counterbalanced submitted to six performance tests and 3D volumetric imaging with both conditions, without mouthguard (NMG) and with the mouthguard (MG). RESULTS: The results demonstrated that the mandibular repositioning mouthguard has a significant effect on both aerobic (p < 0.01) and anaerobic performances (p < 0.05). The MG condition increased the pulmonary ventilation by 9% and maximal aerobic capacity by 5%. In addition, the MG condition increased anaerobic power production by 3% and decreased the 20- and 40-meter sprint time by 4% and 2% respectively. All results were statistically significant (p < 0.05). Imaging results demonstrated a 7 % increase of the upper airway volume when comparing the MG to NMG conditions (p < 0.05). CONCLUSIONS: Our results support our hypothesis that jaw-repositioning custom-made mouthguards can induce an increase in oropharynx width in the upper airways and these changes may be the cause for an increase in athletic performance.
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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 | 0.000 | 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".