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Record W3097290000 · doi:10.3389/fneur.2020.581527

Usefulness of Video Head Impulse Test Results in the Identification of Meniere's Disease

2020· review· en· W3097290000 on OpenAlexaff
Brahim Kaci, Mujda Nooristani, Tamara Mijović, Maxime Maheu

Bibliographic record

VenueFrontiers in Neurology · 2020
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsRoyal Victoria HospitalCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
Fundersnot available
KeywordsElectrocochleographyAudiologyVestibular evoked myogenic potentialMedicineMeniere's diseaseVertigoTinnitusVestibular systemEndolymphatic hydropsReflexPhysical medicine and rehabilitationHearing lossSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Meniere's disease (MD) is an inner ear disorder inducing tinnitus, aural fullness, sensorineural hearing loss, and vertigo episodes. In the past few years, efforts have been made to develop objective measures able to distinguish MD from other pathologies. Indeed, some authors investigated electrophysiological measures, such as electrocochleography and vestibular evoked myogenic potentials or imaging techniques. More recently, the video head impulse test (vHIT) was developed to assess the vestibulo-ocular reflex (VOR). In the last few years, authors aimed at identifying how vHIT may help to identify MD. The objective of this manuscript is to review the different vHIT results in MD patients. We will discuss the usefulness of these findings in the identification of MD, how these results may be explained by pathophysiological mechanisms associated with MD, and finally provide directions for future studies.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.044
GPT teacher head0.306
Teacher spread0.263 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2020
Admission routes1
Has abstractyes

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Same venueFrontiers in NeurologySame topicVestibular and auditory disordersFrench-language works237,207