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Record W2042609062 · doi:10.1186/s40463-015-0065-7

A new diagnostic vestibular evoked response

2015· article· en· W2042609062 on OpenAlexafffund
Zeinab Dastgheib, Brian Lithgow, Brian W. Blakley, Zahra Moussavi

Bibliographic record

VenueJournal of Otolaryngology - Head and Neck Surgery · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsHealth Sciences CentreRiverview HospitalUniversity of Manitoba
FundersMitacs
KeywordsVestibular systemElectrocochleographyOtolithVestibular evoked myogenic potentialAudiologyLinear discriminant analysisMedicineComputer scienceArtificial intelligenceBiologyHearing loss

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the development of a new clinically applicable method for assessing vestibular function in humans with particular application in Meniere's disease. STUDY DESIGN: Sophisticated signal-processing techniques were applied to data from human subject undergoing tilts stimulating the otolith organs and semicircular canals. The most sensitive representatives of vestibular function were extracted as "features". METHODS: After careful consideration of expected response features, Electrovestibulography, a modified electrocochleography, recordings were performed on fourteen Meniere's patients and sixteen healthy controls undergoing controlled tilts. The data were subjected to multiple signal processing techniques to determine which "features" were most predictive of vestibular responses. RESULTS: Linear discriminant analysis and fractal dimension may allow data from a single tilt to be used to adequately characterize the vestibular system. CONCLUSION: Objective, physiologic assessment of vestibular function may become realistic with application of modern signal processing techniques.

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)0.003
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: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.037
GPT teacher head0.279
Teacher spread0.242 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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