The Use Of Custom Fitted Jaw Repositioning Mouthguard During Quiet Standing In Varsity Athletes.
Bibliographic record
Abstract
PURPOSE: Custom fitted jaw repositioning mouthguard (JCM), provided by a dentist do not only protect the teeth, but have shown to greatly affect postural balance in healthy and disease subjects. Yet no studies have measured the role of a JCM on postural balance in athletes. Therefore, the purpose of this study was to examine the effects of a JCM on postural balance among varsity athletes. METHODS: Twenty-one varsity athlete (7 females, 14 males; (Age = 21.6 ± 1.8 years; Height = 177.0 ± 9.4 cm; Weight = 87.6 ± 21.8 kg) participated in this study. The JCM were manufactured on a maxillary model impression of the mandible on the lower surface. Following appraisal of the participants’ teeth impressions, the dentist recorded their occlusal vertical dimension to assess their optimal bite alignment using a phonetic technique. All JCM were fabricated using Erkoflex material. Postural balance was assessed during quiet standing using a Tekscan Mobile Mat. Two 30-seconds static postural balance were taken, one with a JCM and one without (NMG). Center of pressure (COP) area (cm2), distance (cm), variance (cm), anterio-posterior excursion (cm) and the medio-lateral excursion (cm) were collected. Paired-t-tests were used to evaluate performance in postural balance between the two conditions (NMG vs. JCM). RESULTS: COP’s area (1.56 cm2 JCM vs. 2.00 cm2 NMG; p = < 0.01), distance (57.74 cm JCM vs. 62.10 cm NMG; p = 0.01), antero-posterior excursion (2.10 cm JCM vs. 2.41 cm NMG; p = 0.03) and medio-lateral excursion (1.50 cm JCM vs. 1.77 cm NMG; p = 0.03) are all favoring the JCM condition showing less excursions of the COP parameters during quiet standing. CONCLUSIONS: Our results demonstrated that when athletes use a JCM, they have a better postural balance control during quiet standing. These preliminary results suggest that using a JCM could possibly serve as an ergogenic aid to improve performances requiring balance and could help them reduce the risk of injuries.
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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) | 0.001 | 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".