Adults with cochlear implant and vestibular dysfunction: A scoping review
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".