Vestibular evoked myogenic potential testing as an objective measure of vestibular stimulation with cochlear implants
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To determine if vestibular potentials could be elicited with electrical stimulation from cochlear implants. STUDY DESIGN: Prospective cohort study. METHODS: Vestibular responsiveness to electrical stimulation from cochlear implants was assessed via vestibular evoked myogenic potential (VEMP) testing in 53 pediatric and young adult patients. RESULTS: Thirty-one participants (58%) showed at least one vestibular potential in response to acoustic stimulation; 33 (62%) had an electrically evoked vestibular response. A cervical VEMP (cVEMP) was present in 45 of the 96 tested ears (47%) in response to acoustic stimulation, and in 34 ears (35%) with electrical stimulation. An ocular VEMP (oVEMP) was elicited acoustically in 25 ears (26%) and electrically in 34 (35%) ears. In the ears with absent responses to acoustic stimuli, electrically evoked cVEMPs and oVEMPs were present in 14 (27%) and 18 (25%) ears, respectively. Electric VEMPs demonstrated shorter latencies than acoustic VEMPs (P < .01). Whereas an increased prevalence of VEMPs was seen at high stimulation levels (P < .01), there was no difference between prevalence proportions with basal (electrode 3) or apical (electrode 20) stimulation (P > .05). CONCLUSIONS: VEMPs can be elicited with electrical stimulation in a proportion of children with cochlear implants, demonstrating current spread from the cochlea to the vestibular system. The presence of electric VEMPs in acoustically nonresponsive ears, along with the shorter latencies of electrically driven VEMPs, suggests that electrical current can bypass the otoliths and directly stimulate vestibular neural elements. LEVEL OF EVIDENCE: 4. Laryngoscope, 2016 127:E75-E81, 2017.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".