Role of Facial Nerve Motor-Evoked Potential Ratio in Predicting Facial Nerve Function in Vestibular Schwannoma Surgery Both Immediate and at 1 Year
Bibliographic record
Abstract
OBJECTIVE: To determine whether transcranial electrical stimulation-induced facial motor-evoked potential (FMEP) monitoring of the facial nerve (FN) during vestibular schwannoma (VS) tumor resection can predict both immediate and 1 year postoperative FN functional outcome. DESIGN: Prospective consecutive non-comparative observational case series. SETTING: Tertiary referral center. MAIN OUTCOME MEASURES: Facial function, immediate post operation and at 1 year using House-Brackmann (HB) grading scale. METHODS: The study included 367 consecutive patients (men 178; women 189; age 13-81 years) monitored during primary sporadic VS microsurgery between November 2002 and April 2015. Neurofibromatosis type II, revision surgery, previous radiotherapy treatment, preoperative facial nerve weakness, and non-VS cases were excluded retrospectively during analysis of data. Data of facial function were missing from eight patients at 1 year and were excluded. The correlation between the final-to-baseline FMEP ratio and immediate and 1 year facial nerve function was examined. RESULTS: Using logistic regression model, the cut-off points of FMEP ratio were 0.62 (PPV 0.96) and 0.59 (PPV 0.98) which predicted satisfactory FN function (HB grades 1 or 2) immediately postoperative and at 1 year after surgery, respectively. CONCLUSION: Transcranial electrical stimulation FMEP is a valuable tool for monitoring facial nerve function during resection of vestibular schwannoma. Maintaining a FMEP event-to-baseline ratio of 60% or greater is predictive of satisfactory long-term FN function.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".