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Record W4417248551 · doi:10.1080/14670100.2025.2595878

Adults with cochlear implant and vestibular dysfunction: A scoping review

2025· review· en· W4417248551 on OpenAlexaff
Raisa Chowdhury, Alicia Belaiche, Tamara Mijović, Nicolás Pons, Don Nguyen, Emily Kay‐Rivest

Bibliographic record

VenueCochlear Implants International · 2025
Typereview
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsCochlear implantVestibular systemCochlear implantationHearing lossVestibuleImplant

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the rate of vestibular dysfunction after cochlear implantation (CI), identify optimal preoperative testing to detect at-risk patients, examine correlations between objective dysfunction and symptoms, and summarize benefits of postoperative vestibular therapy. METHODS: A scoping review following PRISMA-ScR guidelines was conducted. Searches of PubMed/MEDLINE, Web of Science, Scopus, Embase, and CINAHL identified studies from inception to December 2024. RESULTS: Thirty-five studies including 2,096 adults met criteria. Preoperative vestibular dysfunction in the implanted ear was reported in 47% on caloric testing and 34.9% on cVEMP, increasing postoperatively to 65.9% and 43.1%, respectively. Subjective symptoms rose from 25.1% to 29%, with most resolving by late follow-up. Caloric testing was the most frequently used assessment (66.6%), followed by cVEMP (59.2%) and vHIT (55.5%). Combined paradigms offered the most complete evaluation. Correlation between objective dysfunction and dizziness was weak. Vestibular rehabilitation improved dizziness, balance, and DHI scores in 80-100% of treated patients. DISCUSSION: Most CI recipients experience mild and transient vestibular symptoms, though some require targeted management. CONCLUSION: A substantial proportion of CI recipients develop vestibular dysfunction, supporting the need for comprehensive preoperative assessment.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.825
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.344
Teacher spread0.312 · 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 teacher head, not a consensus.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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