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Record W2809463215 · doi:10.1111/joor.12663

International consensus on the assessment of bruxism: Report of a work in progress

2018· article· en· W2809463215 on OpenAlexaff
Frank Lobbezoo, Jari Ahlberg, Karen G. Raphael, Peter Wetselaar, Alan G. Glaros, Takafumi Kato, Vivian Santiago, Ephraim Winocur, Antoon De Laat, Robert A. de Leeuw, Kiyoshi Koyano, Gilles Lavigne, Peter Svensson, Daniele Manfredini

Bibliographic record

VenueJournal of Oral Rehabilitation · 2018
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de Montréal
FundersNational Institute of Dental and Craniofacial Research
KeywordsSleep BruxismMasticatory forceElectromyographyPhysical medicine and rehabilitationPsychologyMedicinePhysical therapyOrthodontics

Abstract

fetched live from OpenAlex

In 2013, consensus was obtained on a definition of bruxism as repetitive masticatory muscle activity characterised by clenching or grinding of the teeth and/or by bracing or thrusting of the mandible and specified as either sleep bruxism or awake bruxism. In addition, a grading system was proposed to determine the likelihood that a certain assessment of bruxism actually yields a valid outcome. This study discusses the need for an updated consensus and has the following aims: (i) to further clarify the 2013 definition and to develop separate definitions for sleep and awake bruxism; (ii) to determine whether bruxism is a disorder rather than a behaviour that can be a risk factor for certain clinical conditions; (iii) to re-examine the 2013 grading system; and (iv) to develop a research agenda. It was concluded that: (i) sleep and awake bruxism are masticatory muscle activities that occur during sleep (characterised as rhythmic or non-rhythmic) and wakefulness (characterised by repetitive or sustained tooth contact and/or by bracing or thrusting of the mandible), respectively; (ii) in otherwise healthy individuals, bruxism should not be considered as a disorder, but rather as a behaviour that can be a risk (and/or protective) factor for certain clinical consequences; (iii) both non-instrumental approaches (notably self-report) and instrumental approaches (notably electromyography) can be employed to assess bruxism; and (iv) standard cut-off points for establishing the presence or absence of bruxism should not be used in otherwise healthy individuals; rather, bruxism-related masticatory muscle activities should be assessed in the behaviour's continuum.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.224
metaresearch head score (Gemma)0.177
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.224
Threshold uncertainty score0.957

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2240.177
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0120.010
Science and technology studies0.0040.005
Scholarly communication0.0090.007
Open science0.0120.014
Research integrity0.0100.014
Insufficient payload (model declined to judge)0.0040.003

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 head0.053
GPT teacher head0.468
Teacher spread0.415 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods · Commentary

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

Citations1,300
Published2018
Admission routes1
Has abstractyes

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