ArticleClinical neurophysiology practice2026
A practical framework for interpreting intraoperative electrophysiological signals in acoustic neuroma surgery.
Article in Clinical neurophysiology practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To develop a practical, stepwise framework for interpreting intraoperative neurophysiological signals to predict facial nerve outcomes after acoustic neuroma surgery. Methods: This methodological report synthesizes evidence from three IONM modalities: facial motor evoked potentials (FMEPs), direct electrical stimulation (DES), and free-running electromyography (EMG). Key predictive parameters, threshold values, sensitivity, specificity, and technical limitations were extracted from published meta-analyses, systematic reviews, and prospective cohort studies. These parameters were then organized chronologically into a stepwise interpretation framework according to their typical intraoperative use. Results: An end-of-surgery FMEP amplitude reduction >50% predicts poor long-term (≥1 year) facial function with 93% specificity. A DES threshold ≤0.05 mA at the brainstem stump predicts good long-term function (73% sensitivity, 74% specificity). A cumulative A-train duration >10 s on free-running EMG signals high risk for severe immediate palsy (66.7% sensitivity, 88.0% specificity). The 2026 CNS guidelines confirm that electrophysiological parameters predict long-term outcomes better than clinical information alone. Conclusions: This report provides a clinically usable, stepwise framework that integrates FMEP, DES, and free-running EMG thresholds for intraoperative prediction of facial nerve function. Significance: Despite routine use of intraoperative neurophysiological monitoring, no standardized framework has existed to translate real-time signals into postoperative facial nerve predictions. This framework fills that gap by offering surgeons and monitoring teams a clear, evidence-based, and chronologically ordered decision aid, potentially improving early recognition of poor outcomes and facilitating timely referral for facial reanimation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.