MétaCan
Menu
Back to cohort
Record W2529475883 · doi:10.52010/ijom.2014.40.1.3

The masticatory system of the obese: Clinical and electromyographic evaluation

2014· article· en· W2529475883 on OpenAlexaff
Adriana Bueno de Figueiredo, Alfredo Halpern, Márcio C. Mancini, Cíntia Cercato

Bibliographic record

VenueInternational Journal of Orofacial Myology · 2014
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMasticatory forceMedicinePhysical medicine and rehabilitationOrthodontics

Abstract

fetched live from OpenAlex

UNLABELLED: Masticatory performance is determined not only through the speed of mastication, or by the quantity of food ingested; it also depends on the structures and functional integration of the stomatognathic system (SS). OBJECTIVES: This study investigated differences in the SS and orofacial motricity between obese and normal--weight women. METHOD: A total of 18 obese women, with an average age of 28 ± 7.3 years and an average body mass index (BMI) of 37.4 ± 5.1 Kg/m2, and 18 normal--weight women, with an average age of 26 ± 7.6 years and an average BMI of 20.7 ± 1.8 kg/m2, took part in the study. During the speech therapy evaluation, chewing, the number of chewing strokes, and swallowing were observed. The posture, mobility and tonus of lips and tongue, morphology, mobility and tonus of cheeks were designated as normal or altered. The electrical activity of the anterior temporalis, the masticatory muscle was evaluated for both groups using surface electromyography (EMG), which was expressed in microvolts (pV) and registered as Root Mean Squares. RESULTS: Significant differences were found between the two groups in clinical evaluation. In surface EMG, the obese group showed asymmetry of electrical activity of the anterior temporalis. CONCLUSION: This study suggests that speech therapist investigation of the SS should be combined with interdisciplinary obesity management.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.035
GPT teacher head0.436
Teacher spread0.401 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Orofacial MyologySame topicTemporomandibular Joint DisordersFrench-language works237,207