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Record W2892312655 · doi:10.4081/audiores.2018.212

State-of-the-Art Assessment Allows for Improved Vestibular Evoked Myogenic Potential Test-Retest Reliability

2018· article· en· W2892312655 on OpenAlexafffund
L. Behtani, Maxime Maheu, Audrey Delcenserie, Mujda Nooristani, François Champoux

Bibliographic record

VenueAudiology Research · 2018
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsVestibular evoked myogenic potentialReliability (semiconductor)Vestibular systemPhysical medicine and rehabilitationAudiologyElectromyographyTest (biology)Medicine

Abstract

fetched live from OpenAlex

The goal of the present study was to evaluate the test-retest reliability values of myogenic responses using the latest guidelines for vestibular assessment. Twenty-two otologically and neurologically normal adults were assessed twice, on two different days. The analyses were carried out using interclass correlations. The results showed that the latest recommendations for vestibular assessment lead to test-retest reliability values that are as high, or greater, than those reported in previous studies. The results suggest that state-of-the-art testing, using the latest recommendations as well as electromyography control, improves reliability values of myogenic responses, more specifically for the cervical vestibular evoked myogenic potentials. The impact of small differences in experimental procedures on the reliability values of myogenic responses is also addressed.

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 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)0.019
metaresearch head score (Gemma)0.039
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.039
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.371
Teacher spread0.329 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations1
Published2018
Admission routes2
Has abstractyes

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