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Record W4400158451 · doi:10.1093/ejo/cjae030

The effect of clear aligner treatment on masticatory muscles (masseter, temporalis) activity in adults: a systematic review and meta-analysis

2024· review· en· W4400158451 on OpenAlexaboutno aff
Roberta Lekavičiūtė, Smiltė Paldauskaitė, Simona Stučinskaitė, Giedrė Trakiniené

Bibliographic record

VenueEuropean Journal of Orthodontics · 2024
Typereview
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineElectromyographyMasticatory forceMasseter muscleCochrane LibraryMasticationTemporalis muscleObservational studyOrthodonticsDentistryTemporal muscleMeta-analysisRandomized controlled trialPhysical medicine and rehabilitationSurgeryInternal medicine

Abstract

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BACKGROUND: The use of clear aligners is becoming more common for aesthetic orthodontic treatment, but there are still concerns about how they affect mastication biomechanics in the short and long term. The clear aligners treatment (CAT) mechanism changes the position of the mandible and maxilla, especially impacting the masseter muscle. Surface electromyography (sEMG) proves to be a useful method to evaluate masticatory muscle activity (MMA). OBJECTIVES: To analyze the effect of clear aligners treatment on alterations in masticatory muscles (masseter, temporalis) using surface electromyography. SEARCH METHODS: Five databases (PubMed, Web of Science, SCOPUS, Cochrane Library, and Google Scholar) were searched up to March 2024. SELECTION CRITERIA: Studies in which MMA was evaluated after the installation of orthodontic clear aligners. DATA COLLECTION AND ANALYSIS: Screening, data extraction, and quality assessments were performed by four investigators independently. The data, which evaluated temporalis and masseter muscle characteristics during CAT using surface electromyography, was extracted, and the quality of the studies was evaluated. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). RESULTS: Six studies (two prospective cohort studies, three observational longitudinal studies, and one observational longitudinal case-control study) with low and moderate risk of bias were included in the qualitative synthesis. Six of these were also included in the meta-analysis. Our study investigated the dynamics of masseter and temporalis muscle activity during CAT. The results show that during maximal voluntary clenching, the masseter muscle demonstrated a significant initial increase (P < .05) followed by a subsequent non-significant decrease. It also showed that submaximal voluntary clenching consistently exhibited a significant reduction in muscle activity throughout the study period (P < .01). Assessment of muscle activity at the mandibular resting position revealed a variety of responses, with some participants showing a significant increase while others exhibited non-significant changes (P < .05, P > .05, respectively). However, the meta-analysis showed a non-significant difference in measuring masseter and temporalis muscles activity during CAT. CONCLUSIONS: Based on existing evidence, it is reasonable to conclude that CAT affected MMA. During maximal voluntary clenching, masseter muscle activity initially increased but later decreased, while temporalis muscle activity showed mixed results. Submaximal voluntary clenching revealed a consistent decrease in muscle activity over time. Mandibular resting position assessments showed both increases and no significant changes in muscle activity. However, the existing literature is insufficient to draw concrete conclusions; therefore, well-conducted further research is needed to confirm this statement. REGISTRATION: This systematic review and meta-analysis were registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42024522231).

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.026
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.026
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.129
GPT teacher head0.439
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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