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Record W4322758162 · doi:10.1097/aud.0000000000001343

Utility of Inter-Frequency Amplitude Ratio of Vestibular-Evoked Myogenic Potentials in Identifying Meniere’s Disease: A Systematic Review and Meta-Analysis

2023· review· en· W4322758162 on OpenAlexaboutno aff
Niraj Kumar Singh, Prawin Kumar, Nirmala Jagadish, Akshay Mendhakar, Yatin Mahajan

Bibliographic record

VenueEar and Hearing · 2023
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMeniere's diseaseVestibular evoked myogenic potentialAudiologyMedicineMeta-analysisDiseaseSystematic reviewOtolithVestibular systemSacculePopulationMEDLINEInternal medicineBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: A recently devised parameter of vestibular-evoked myogenic potential (VEMP) based on the principles of frequency tuning is the inter-frequency amplitude ratio (IFAR). It refers to the ratio of the amplitude of 1000 Hz tone burst evoked VEMP to 500 Hz evoked tone burst. A pathology like Meniere's disease changes the frequency response and alters the frequency tuning of the otolith organs. Because IFAR is based on the principle of frequency tuning of VEMP, it is likely to help identify Meniere's disease. Few studies in the last decade have investigated the utility of IFAR in identifying Meniere's disease. However, a systematic review and a meta-analysis on IFAR in Meniere's disease are lacking. The present study investigates whether the IFAR of VEMP helps identify Meniere's disease and differentiates it from healthy ears and other vestibular pathologies. DESIGN: The present study is a systematic review and a meta-analysis. The studies investigating the IFAR of cervical and ocular VEMPs in Meniere's disease, healthy controls, and other vestibular pathologies were searched across research databases such as PubMed, Science Direct, and Scopus. The search strategy was developed using the PICO (population, intervention, comparison, and outcomes) format, and Medical Subject Headings (MeSH) terms and Boolean operators were employed. The systematic review was performed using the Rayyan software, whereas the Review Manager software was used to carry out the meta-analysis. A total of 16,605 articles were retrieved from the databases. After the duplicate removal, 2472 articles remained. These were eliminated using title screening, abstract screening, and full-length inspections. A total of nine articles were found eligible for quality assessment and meta-analysis, and the New Castle-Ottawa Scale was used for quality assessment. After the data extraction, 24 six articles were found to have the desired data format for the meta-analysis. RESULTS: The results showed significantly higher IFAR in the affected ears of individuals in the Meniere's disease group than in the control group's unaffected ears. There was no significant difference between the unaffected ears of individuals in the Meniere's disease group and the ears of the control group. The only study on Meniere's disease and benign paroxysmal positional vertigo found significantly larger ocular VEMP IFAR in ears with Meniere's disease than in benign paroxysmal positional vertigo. CONCLUSIONS: This systematic review and meta-analysis found IFAR efficient in differentiating Meniere's disease from healthy controls. We also found an enhanced IFAR as a potential marker for Meniere's disease. However, more investigations are needed to confirm the utility of an enhanced IFAR value in the exclusive identification of Meniere's disease.

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)0.015
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.019
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.042
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0190.032
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.230
GPT teacher head0.393
Teacher spread0.163 · 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 designMeta-analysis
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

Citations9
Published2023
Admission routes1
Has abstractyes

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