Electromyographic Muscular Activity Improvement in Class II Patients Treated with the Pre-Orthodontic Trainer
Bibliographic record
Abstract
OBJECTIVE: A study was designed to determine changes in the amplitude of the EMG muscular activity of the Masseter and Temporalis muscles at clench in children with a Class II, division 1 malocclusion treated with the pre-orthodontic Trainer functional appliance, for 12 months. STUDY DESIGN: 36 Class II, division 1 malocclusion patients (mean age 7.6 ± 1.3 years) composed the treated group and wore the functional appliance; 22 children with a similar age and malocclusion composed the untreated controls; and, 20 children with no dental malocclusion participated as normal controls. Electromyographic (EMG) muscular activity of the Temporalis and Masseter muscles were recorded before and after treatment. RESULTS: Subjects in the treated group reported a bilateral significant increase in the muscular electrical activity in the both tested muscles (p < 0.001). After treatment, they recorded values similar to those measured in normal controls, whereas the untreated controls remained on lower values. CONCLUSION: These results confirm that treatment with the pre-orthodontic Trainer functional appliance significantly increases the EMG muscular activity in the Temporalis and Masseter muscles at clench in patients with Class II, division 1 malocclusion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".