Electromyographic Assessment of Muscle Activity in Children Undergoing Orthodontic Treatment—A Systematic Review
Bibliographic record
Abstract
Background: Surface electromyography (sEMG) can provide an objective and quantitative image of the functional state of neuromuscular balance in the stomatognathic system. The objective of this systematic review is to examine current scientific evidence regarding the effects of orthodontic treatment on muscle electromyographic (EMG) activity in children. Methods: The search strategy included the PubMed, PubMed Central, Web of Science, Scopus, and Embase databases. The inclusion criteria were studies assessing EMG muscle activity in children undergoing orthodontic treatment compared with untreated children. The Cochrane risk-of-bias tool (RoB2) and the Newcastle–Ottawa Scale (NOS) were used to evaluate the quality of the studies. The quality of evidence assessment was performed using GRADE analysis. The PRISMA diagram visually represented the search strategy, as well as screening and inclusion process. Results: The search strategy identified 540 potential articles. Fourteen papers met the inclusion criteria. Six studies were judged at a low risk of bias. The certainty of evidence was rated as moderate to low, according to the GRADE criteria. Studies showed alterations in EMG muscle activity in children undergoing orthodontic treatment. Conclusions: Orthodontic treatment appears to affect muscle activity in children undergoing orthodontic treatment. However, the quality of evidence is low and, therefore, it is not possible to definitively state this effect. Further long-term studies are needed to confirm the findings of this review. Study protocol number in PROSPERO database: CRD42023491005.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.006 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".