Checking the manufacturing of Simões Network 10 – SN10 through surface electromyography (sEMG) – case report study
Bibliographic record
Abstract
Use of functional orthopedic appliances (FOA) in the treatment of malocclusion and Temporomandibular Disorders (TMD) has been proved to be effective but there is still questions to be answered like the muscular action of the referred appliances. The aim of this study is checking through a proven protocol of surface electromyography (sEMG) to study muscular action of FOA to check to check if it is correctly manufactured. The appliance studied is a Simões Network 10 – SN10 to treat Class II malocclusion of retrognathia. The sEMG was collected 1 patients with class II malocclusion with retrognathia who belong to a 164 volunteers with malocclusion, in two times T1 before installation of the FOA in mouth, T2 15 minutes after the FOA installation in the mouth. sEMG data of bilateral masseter, bilateral temporal and bilateral suprahyoid muscles using conditioner signals 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 were used for the sEMG measurements. Frequency calibration was 2000 Hz, with 2048 sample by channel and time 1,024 seconds, and filters regulation was 20 Hz and 1000 Hz. With the FOA in the mouth all measurements improved with a more simetrical sEMG in T2 in rest and isometric contraction measurements. The protocol used to check the manufacturing of functional orthopedic appliances using surface EMG proved to be a valid tool in this case report study. Further investigations are needed to confirm this protocol and check if the same happens with other types of functional orthopedics appliances.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".