Patients With Temporomandibular Disorders Have Increased Fatigability of the Cervical Extensor Muscles
Bibliographic record
Abstract
OBJECTIVES: To determine whether patients with myogenous and mixed temporomandibular disorders (TMD) have greater fatigability of the cervical extensor muscles while performing a neck extensor muscle endurance test (NEMET) when compared with healthy controls. METHODS: A total of 151 individuals participated in this study. Of these 47 were healthy controls, 57 patients had myogenous TMD, and 47 patients had mixed TMD. All patients performed the NEMET. The patients were instructed to maintain a prone lying position with the neck unsupported as long as possible, stopping at signs of fatigue or any discomfort. Electromyographic activity of the cervical extensor muscles during the NEMET and the holding time were collected for all patients and were compared across groups. A 1-way analysis of variance was used to evaluate the differences in holding time between patients with TMD and healthy controls. A mixed model analysis was used to evaluate the differences in normalized median frequency at different times (fatigue index) for the cervical extensor muscles while performing the NEMET between patients with TMD and controls. RESULTS: There were statistically significant differences (P<0.05) in the slopes of the normalized median frequency between patients with TMD and healthy controls at 10, 30, 40, 50, 60, 70, 80, 90, and 100 seconds of the NEMET. Holding time was significantly reduced in both patients with myogenous TMD and mixed TMD when compared with healthy controls (P<0.05). DISCUSSION: These results highlight the fact that alterations of endurance capacity of the extensor cervical muscles could be implicated in the neck-shoulder disturbances presented in patients with TMD.
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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".