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Record W3181681392 · doi:10.1002/jaba.864

Electromyography of diurnal bruxism during assessment and treatment

2021· article· en· W3181681392 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Applied Behavior Analysis · 2021
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of Manitoba
FundersUniversity of AucklandManitoba Health Research Council
KeywordsElectromyographyContext (archaeology)PsychologyPhysical medicine and rehabilitationIdentification (biology)Set (abstract data type)PopulationAudiologyMedicineNeuroscienceComputer science

Abstract

fetched live from OpenAlex

Diurnal bruxism among individuals with intellectual disabilities is often measured on the basis of its auditory products, thereby precluding the contingent presentation of stimuli during silent bruxism events. Electromyography (EMG) offers a technological solution to the identification of all bruxism events. EMG has not been previously evaluated in nonvocal clients with intellectual disabilities in the context of functional analysis and treatment. In the current series of analyses, we suggest a set of methods to implement EMG technology with this population. In Analysis 1, we propose a strategy for systematically identifying bruxism events. In Analysis 2 we evaluate an EMG staff-training package with naïve interventionists without past experience with EMG technology. Finally, Analysis 3 presents a practical example of this method during the functional analysis and treatment of a client with frequent diurnal bruxism.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.740
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.245
Teacher spread0.236 · 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