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Record W4324336272 · doi:10.31459/turkjkin.1232047

The effect of different jaw positions on upper extremity performance, core endurance, and postural stability: A cross-sectional study

2023· article· en· W4324336272 on OpenAlexaboutno aff
Senem Demirdel, Gülşah GÜL, Öznur GÜMÜŞ, Betül Kuz

Bibliographic record

VenueTurkish Journal of Kinesiology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsTrunkCore (optical fiber)Balance (ability)MedicinePhysical medicine and rehabilitationCore stabilityPhysical therapyRehabilitationTest (biology)Balance testOrthodonticsComputer science

Abstract

fetched live from OpenAlex

The jaw sensorimotor system has functional relationships with other parts of the body through neuroanatomical and biomechanical interactions. The aim of this study was to examine the effects of different jaw positions on upper extremity performance, core endurance, and postural stability. This cross-sectional study included 49 healthy young adults aged 18-28 years. Upper extremity performance was evaluated using the push-up test, core endurance was evaluated using the McGill’s Trunk Flexion and Extension Test and postural stability was evaluated using the Balance Error Scoring System. All assessments were made with resting jaw and clenching jaw positions. There was no significant difference in the Push-up test and Balance Error Scoring System scores in different jaw positions (p>0.05). The Mcgill trunk flexion test and extension test performance were found to be better in the clenched jaw position (p=0.017, p=0.035, respectively). The results of this study showed that jaw clenching increases core endurance time. This result should be taken into account when planning an evaluation and rehabilitation program for the core area.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.049
GPT teacher head0.394
Teacher spread0.345 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTurkish Journal of KinesiologySame topicTemporomandibular Joint DisordersFrench-language works237,207