Infuence of dynamic electrical neurostimulation on the functional state of the masticatory muscles in the treatment of patients with temporomandibular joint pain dysfunction syndrome
Bibliographic record
Abstract
Objective: to determine the efectiveness of dynamic electrical stimulation on the functional state of the masticatory muscles in patients with temporomandibular joint pain dysfunction syndrome. Material and methods. The study involved 60 people aged 25 to 60 years with temporomandibular joint pain dysfunction syndrome, who were divided into two groups of 30 people. Patients of the frst group underwent dynamic electrical nerve stimulation in the area of pain complaints, and patients of the second group also in the projection of the trigeminal nerve exit on the face. Elec-tromyographic examination of the masticatory muscles and completion of the McGill pain questionnaire as modifed by V.V. Kuzmenko et al. before and after exposure to dynamic electrical nerve stimulation were performed by all patients. Results. All patients showed a signifcant improvement in the condition of the masticatory and temporal muscles according to electromyography p<0.001 in all measurements, the inclusion of additional zones did not afect the improvement in the condition. According to the pain questionnaire, the treatment results improved in both groups (p<0.001), M±m=10.7±0.89 points and M±m=5.87±0.81 points before and after treatment in the group1, M±m=10.83±0.86 and M±m=5.73±0.65 points before and after treatment in the group 2. When comparing results between groups, there were no signifcant diferences. Conclusion. Dynamic electrical neurostimulation has a positive clinical efect on the functional state of the masticatory and temporal muscles after just one session.
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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".