Facial nerve electromyography: a useful tool in detecting nonauditory side effects of cochlear implantation.
Bibliographic record
Abstract
BACKGROUND: Facial nerve stimulation is a known complication of cochlear implantation. Until recently, the assessment of facial nerve stimulation was primarily based on self reports which can be problematic in a pediatric setting. For children with facial nerve stimulation, the long-term implications implant use are unclear. OBJECTIVES: To illustrate the clinical utility of facial electromyography (EMG) in avoiding nonauditory stimulation evoked by cochlear implant use and to assess the potential for change in these responses over time with chronic electrical stimulation. METHODS: Midline evoked auditory brainstem responses and surface EMG of the facial musculature were performed in three children suspected clinically of having electrically evoked facial nerve stimulation. Each child had a different clinical presentation; the first complained of pain, the second experienced involuntary facial movements, and the third experienced nonspecific discomfort after sequential bilateral cochlear implantation. RESULTS: All three children had EMG evidence of facial nerve stimulation. However, based on thresholds and patterns of responses in specific facial nerve branches, stimulation was responsible for the subjective and behavioural responses in only two of the three children. CONCLUSIONS: Pediatric cochlear implant users are unreliable in their reports of facial nerve stimulation. Evoked surface EMG is an objective and useful clinical tool in patients with known or suspected facial nerve stimulation and can lead to improved implant use.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".