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Record W2003627718 · doi:10.1111/joor.12216

Association between near occlusal contact areas and mixing ability

2014· article· en· W2003627718 on OpenAlexaff
Tomoki Horie, Makoto Kanazawa, Yuriko Komagamine, Yohei Hama, Shunsuke Minakuchi

Bibliographic record

VenueJournal of Oral Rehabilitation · 2014
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsMcGill University Health Centre
FundersTokyo Medical and Dental University
KeywordsMolarPremolarColorimeterDentistryMandibular first molarOrthodonticsDental occlusionMaterials scienceSiliconeMedicineOcclusionComposite materialOptics

Abstract

fetched live from OpenAlex

This study investigated the relationship between occlusal contact and near contact areas defined by clenching intensity using electromyograms (EMGs) and mixing ability assessed with colour-changeable chewing gum. Participants comprised 44 dentate adults (24 men, 20 women) with a mean age of 28·2 ± 6·8 years. Silicone material was used to measure the occlusal contact and near contact areas (the area of each type of tooth, the total area of the first molar and second molar, the second premolar to the second molar and the first premolar to the second molar) defined by clenching intensity at 10% maximum voluntary contraction (MVC). Colour-changeable chewing gum was used to assess mixing ability. A colorimeter was used to measure colour changes, and the calculated colour difference (ΔE) was used as a measure of mixing ability. Correlation analysis of ΔE and occlusal contact and near contact areas revealed a significant positive correlation of 0·47 at 0-160 μm thicknesses of the silicone registration material of the second molar (P < 0·01). The near contact area with a thickness up to 200 μm was correlated with mixing ability, with the correlation strengthening as the interocclusal distance increased up to 160 μm. Notably, occlusal contact and near contact areas of the second molar were strongly correlated with mixing ability in dentate adults.

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)0.006
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.012
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 science0.0000.000
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 head0.023
GPT teacher head0.372
Teacher spread0.349 · 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.

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

Citations37
Published2014
Admission routes1
Has abstractyes

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