Relationship Between Functional Movements of the Mandible and Core Stability in Young Healthy Adults
Bibliographic record
Abstract
Background. Temporomandibular joints (TMJs) have a common neuromuscular connection with neck and scapula, so dysfunction of one or both joints can lead to changes in the spine posture and vice versa. Due to the compensatory mechanism, occurring following TMJ functional disorders, the balance between facial and neck muscle activity is disrupted. A change in any biomechanical unit inevitably results in a change of the posture control system, but there is a lack of research evaluating the relationship between functional movements of the TMJ and trunk stability. The aim. To determine the relationship between core stability and functional movements of the mandible. Methods. The study included 20 participants aged between 20 and 40 years. Participants were tested individually. Tests and measurements selected for the study: trunk stability assessment by functional Dynamic Neuromuscular Stabilization (DNS) tests; assessment of static trunk muscle endurance by McGill endurance tests; assessment of TMJ range of motion using a ruler. Results. Participants performed best on the DNS Supine test with legs raised up, and performed worst on the squat, bear position tests and trunk extension static endurance test. 90% of the participants had impaired range of motion of mandible protrusion, 60% had mandibular depression limitations. The static endurance of trunk flexion was 136.85±96.97 s, extension – 141.45±94.52 s, left side– 98.00±76.08 s, right side– 99.95±96, 99 s. Conclusion. There are strong, moderate and weak linear inverse functional relationships between trunk stability and TMJ mobility. The weaker the core stability, the more restricted the mobility of the mandible. Keywords: temporomandibular joint, TMJ dysfunction, dynamic neuromuscular stabilization, core stability.
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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 | 0.001 | 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) | 0.003 | 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".