Determinated area (DA) treatment goal when treating malocclusions with jaw functional orthopedics (JFO): Contribution to scientific evidence
Bibliographic record
Abstract
One of the goals of malocclusion treatment with Jaw Functional Orthopedics (JFO) is to obtain touch of the inferior incisors against the superior incisors in Determinate Area (DA). It is postulated that this touch would bring better stimuli and consequently faster results. Surface electromyographic study of the muscles was performed in Masseter, Temporal and suprahyoid muscles bilaterally from 159 patients undergoing malocclusion treatment with functional orthopedic appliances (FOA). To record muscle electric activity were used a conditioning signal module from Lynx Electronics Ltda with 8 channels, model EMG1000; software AqDAnalysis 4,18 from Lynx Electronics Ltda.; Software Lynx BioInspector 1,8r; passive surface electrodes (Ag/AgCl) from Noraxon Dual Electrodes (USA); dischargeable reference electrodes Kendall Meditrace (Ag/AgCl) – Canada. Frequency calibration was 2000 Hz, with 2048 sample by channel and time 1,024 seconds, and filters regulation was 20 Hz and 1000 Hz. The results found prove that there is a better electromyographic activity (bilaterally balanced) of the studied muscles when there is touch on DA. Conclusion. Touch in DA improves the neuromuscular response of patients treated with JFO. sEMG is a trustable tool to analyze masticatory muscles function for Diagnosis, Treatment and evaluation post treatment/gained objective.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".