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Record W1594302055 · doi:10.5772/39192

On Temporomandibular Joint Sound Signal Analysis Using ICA

2012· book-chapter· en· W1594302055 on OpenAlexaff
Feng Jin, Farook Sattar

Bibliographic record

VenueInTech eBooks · 2012
Typebook-chapter
Languageen
FieldComputer Science
TopicBlind Source Separation Techniques
Canadian institutionsUniversity of WaterlooToronto Metropolitan University
Fundersnot available
KeywordsJoint (building)Sound (geography)Independent component analysisTemporomandibular jointSIGNAL (programming language)AcousticsComputer scienceOrthodonticsArtificial intelligenceMedicinePhysicsEngineering

Abstract

fetched live from OpenAlex

The Temporomandibular Joint (TMJ) is the joint which connects the lower jaw, called the mandible, to the temporal bone at the side of the head. The joint is very important with regard to speech, mastication and swallowing. Any problem that prevents this system from functioning properly may result in temporomandibular joint disorder (TMD). Symptoms include pain, limited movement of the jaw, radiating pain in the face, neck or shoulders, painful clicking, popping or grating sounds in the jaw joint during opening and/or closing of the mouth. TMD being the most common non-dental related chronic source of oral-facial pain(Gray et al., 1995)(Pankhurst C. L, 1997), affects over 75% of the United States population(Berman et al., 2006). TMJ sounds during jaw motion are important indication of dysfunction and are closely correlated with the joint pathology(Widmalm et al., 1992). The TMJ sounds are routinely recorded by auscultation and noted in dental examination protocols. However, stethoscopic auscultation is very subjective and difficult to document. The interpretations of the sounds often vary among different doctors. Early detection of TMD, before irreversible gross erosive changes take place, is extremely important.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.051
GPT teacher head0.281
Teacher spread0.230 · 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 designTheoretical or conceptual
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
Published2012
Admission routes1
Has abstractyes

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